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Journal ArticleDOI

A cloud-removal algorithm for SSM/I data

01 Jan 1999-IEEE Transactions on Geoscience and Remote Sensing (IEEE)-Vol. 37, Iss: 1, pp 54-62

AbstractMicrowave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager (SSM/I) data with the aid of new algorithms to eliminate small-scale distortion caused by clouds or precipitation. Mean, second-highest value, modified maximum average (MMA), and hybrid compositing algorithms are compared. The effectiveness of each algorithm is illustrated through simulation and real data distribution analysis. The results show that the second-highest value algorithm is biased high. MMA provides a more accurate brightness temperature estimate in areas of atmospheric distortion, while the mean is superior in regions with little or no distortion. A hybrid algorithm is developed that is a combination of MMA and mean. It utilizes the strengths of both to create a superior algorithm for regions with varying levels of distortion. Uses of composite images produced by these algorithms include studies of vegetation change, land cover classification, and surface parameter extraction.

Summary (2 min read)

Introduction

  • Because the surface brightness observed by the SSM/I may be adversely affected by spatial variations in the atmospheric profile over the surface, algorithms for cloud removal have been developed [1], [10].
  • The authors compare several new algorithms that generate cloud-free composite images from multiple passes of the study region.
  • Simulations to determine the effectiveness of these algorithms are performed.
  • Section IV introduces the modified maximum average (MMA) and hybrid Manuscript received November 18, 1996; revised October 27, 1997.

II. BACKGROUND

  • The SSM/I is a total-power, seven-channel, four-frequency radiometer [5].
  • It utilizes an integrate-and-dump filter as the antenna scans the ground track [7].
  • The 3-dB antenna footprints, which are different for each frequency, generally have an elliptical shape on the surface of the earth due to the elevation angle of the radiometer [6].
  • The most crucial factors affecting a radiometric measurement, however, are the surface emissivity and temperature, the vegetation canopy, and the atmospheric conditions [19].

III. GENERATION OF CLOUD-FREE IMAGES

  • One of the challenges in mapping the surface brightness from spaceborne radiometer data is atmospheric distortion.
  • Authorized licensed use limited to: Brigham Young University.
  • Clouds and precipitation affect surface brightness measurements in two ways.
  • Since each frequency has a different footprint size, using lower frequency data to remove atmospheric distortion effects in higher frequency channels may unnecessarily exclude undistorted values in the higher frequency channels [1].
  • Thus, the second-highest value technique’s ability to reduce noise is strongly influenced by the measurement distribution.

IV. MMA ALGORITHM

  • This algorithm attempts to estimate the cloud-free surface brightness of a pixel by choosing a subset of pixel values from the ensemble of measurements of that pixel and then averaging the selected values together.
  • To select pixel values from the ensemble in the MMA technique, the sample mean of the entire pixel ensemble is first computed.
  • The remaining values consist of those values that are above the ensemble mean but less than the maximum value of the ensemble.
  • To compare the variances of the MMA algorithm and the second-highest value technique, consider Fig. 2(b).
  • In the standard deviation image of Fig. 4, the areas with low values correspond to regions with little or no atmospheric distortion.

V. SIMULATION

  • To further compare and contrast the mean, second-highest value, MMA, and hybrid algorithms, a simple Monte Carlo analysis for a single pixel is presented.
  • This simulation assumes that the true pixel brightness for a geographical area is 280 K.
  • Two of the ensemble measurements then have simulated atmospheric distortion added.
  • The windowed mean is the mean of values within one standard deviation of the ensemble mean.
  • The simulation results indicate that MMA is superior in the presence of significant distortion and mean is best with little or no distortion present, while the hybrid algorithm combines the two in a manner that uses the appropriate algorithm for each pixel.

VI. SSM/I DATA ANALYSIS

  • To validate the algorithms with actual data, a region of the Amazon Basin was chosen for SSM/I data analysis.
  • Authorized licensed use limited to: Brigham Young University.
  • These distributions demonstrate the difference between cloudy and clear regions.
  • The authors note that the mean image has the highest standard deviation.
  • Their similar performance indicates that hybrid primarily used the MMA algorithm in the presence of atmospheric distortion.

VII. CONCLUSION

  • A comparison of several different methods (mean, secondhighest value, MMA, and hybrid) for creating cloud-free temporal composite surface brightness temperature images from SSM/I has been presented.
  • Taking the mean is optimum in the absence of clouds and hydrometeors.
  • The second-highest value algorithm removes distorted pixel brightness temperature measurements, but it has an inherent high bias in its brightness temperature distribution.
  • The MMA algorithm more accurately estimates the desired value and has a lower variance; however, it may have an undesirable bias for instances in which no distortion is present.
  • The authors note that in the presence of persistent atmospheric distortions, the distortion cannot be removed.

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Brigham Young University Brigham Young University
BYU ScholarsArchive BYU ScholarsArchive
Faculty Publications
1999-01-01
A cloud-removal algorithm for SSM/I data A cloud-removal algorithm for SSM/I data
David G. Long
david_long@byu.edu
Douglas L. Daum
Quinn P. Remund
Follow this and additional works at: https://scholarsarchive.byu.edu/facpub
Part of the Electrical and Computer Engineering Commons
Original Publication Citation Original Publication Citation
Long, D. G., Q. P. Remund, and D. L. Daum. "A Cloud-Removal Algorithm for SSM/I Data."
Geoscience and Remote Sensing, IEEE Transactions on 37.1 illustrated through simulation and
real data distribution analysis. The results show that the second-highest value algorithm is
biased high. MMA provides a more accurate brightness temperature estimate in areas of
atmospheric distortion, whil(TRUNCATED) (1999): 54-62
BYU ScholarsArchive Citation BYU ScholarsArchive Citation
Long, David G.; Daum, Douglas L.; and Remund, Quinn P., "A cloud-removal algorithm for SSM/I data"
(1999).
Faculty Publications
. 631.
https://scholarsarchive.byu.edu/facpub/631
This Peer-Reviewed Article is brought to you for free and open access by BYU ScholarsArchive. It has been
accepted for inclusion in Faculty Publications by an authorized administrator of BYU ScholarsArchive. For more
information, please contact ellen_amatangelo@byu.edu.

54 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 37, NO. 1, JANUARY 1999
A Cloud-Removal Algorithm for SSM/I Data
David G. Long, Senior Member, IEEE, Quinn P. Remund, and Douglas L. Daum
AbstractMicrowave radiometers, while traditionally utilized
in atmospheric and oceanic studies, can also be used in land
surface applications. However, the problem of undesirable at-
mospheric effects caused by clouds and precipitation must be
addressed. In this paper, temporal composite surface bright-
ness images are generated from special sensor microwave/imager
(SSM/I) data with the aid of new algorithms to eliminate small-
scale distortion caused by clouds or precipitation. Mean, second-
highest value, modified maximum average (MMA), and hybrid
compositing algorithms are compared. The effectiveness of each
algorithm is illustrated through simulation and real data distri-
bution analysis. The results show that the second-highest value
algorithm is biased high. MMA provides a more accurate bright-
ness temperature estimate in areas of atmospheric distortion,
while the mean is superior in regions with little or no distortion.
A hybrid algorithm is developed that is a combination of MMA
and mean. It utilizes the strengths of both to create a superior
algorithm for regions with varying levels of distortion. Uses of
composite images produced by these algorithms include stud-
ies of vegetation change, land cover classification, and surface
parameter extraction.
Index Terms Cloud removal, compositing, electromagnetic
atmospheric interference, microwave radiometry.
I. INTRODUCTION
M
ICROWAVE radiometers, such as the special sensor
microwave/imager (SSM/I) [5], [6] have wide applica-
tion in atmospheric remote sensing over the ocean and provide
essential inputs to numerical weather prediction models. SSM/I
data have also been used for land and ice studies, including
measurements of snow cover classification [4], soil and plant
moisture content [8], [15], atmospheric moisture over land
[10], land surface temperature [12], and mapping polar ice
[18].
Because the surface brightness observed by the SSM/I may
be adversely affected by spatial variations in the atmospheric
profile over the surface, algorithms for cloud removal have
been developed [1], [10]. In this paper, we compare several
new algorithms that generate cloud-free composite images
from multiple passes of the study region. Simulations to
determine the effectiveness of these algorithms are performed.
Actual SSM/I data are analyzed by exploring the effects of
compositing algorithms on the pixel surface brightness temper-
ature distributions. This paper is organized as follows. After a
brief background discussion in Section II, Section III discusses
the production of no-cloud composite images. Section IV
introduces the modified maximum average (MMA) and hybrid
Manuscript received November 18, 1996; revised October 27, 1997.
The authors are with Brigham Young University, Provo, UT 84602-4099
USA (e-mail: long@ee.byu.edu).
Publisher Item Identifier S 0196-2892(99)00024-8.
TABLE I
SSM/I C
HANNELS
algorithms. A simulation experiment to compare the cloud-
removal algorithms is presented in Section V. Section VI
discusses the analysis of actual SSM/I data. Finally, the
conclusions are given.
II. B
ACKGROUND
The SSM/I is a total-power, seven-channel, four-frequency
radiometer [5]. The channels are horizontal and vertical polar-
izations at 19.35, 37.0, and 85.5 GHz and vertical polarization
at 22.235 GHz. It utilizes an integrate-and-dump filter as
the antenna scans the ground track [7]. The 3-dB antenna
footprints range from about 15 to 70 km in the along-track
direction and 13 to 43 km in the cross-track direction (see
Table I). The 3-dB antenna footprints, which are different
for each frequency, generally have an elliptical shape on the
surface of the earth due to the elevation angle of the radiometer
[6].
The brightness temperatures observed by the SSM/I are a
function of the effective brightness temperature of the earth’s
surface and the emission, scattering, and attenuation of the
atmosphere. Because of the spatial and temporal variability of
the surface brightness, which is a function of the properties of
the soil and overlaying vegetation and their physical tempera-
tures, it is difficult to decompose the observed brightness into
its individual components. The most crucial factors affecting a
radiometric measurement, however, are the surface emissivity
and temperature, the vegetation canopy, and the atmospheric
conditions [19].
III. G
ENERATION OF CLOUD-FREE IMAGES
One of the challenges in mapping the surface brightness
from spaceborne radiometer data is atmospheric distortion.
Cloud cover and precipitation are two primary sources of this
distortion. Although cloud and rain cause little microwave
attenuation for frequencies less than 10 GHz, the higher
microwave frequencies of the SSM/I (19.35, 22.235, 37.0, and
85.0 GHz) show substantial atmospheric loss due to scattering
0196–2892/99$10.00 © 1999 IEEE
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LONG et al.: CLOUD-REMOVAL ALGORITHM FOR SSM/I DATA 55
Fig. 1. Individual SSM/I swath examples of temporal atmospheric distor-
tions. These images were created by assigning the closest measurement value
to each pixel. Columns (from left to right) correspond to 1992 Julian Days
245, 248, 261, and 264 of the passes. Rows (from top to bottom) indicate
SSM/I channels 19 V, 22 V, 27 V, and 85 V.
from hydrometeors and water vapor. Over the ocean, the
atmospheric signal is used to deduce cloud water content
from the change in brightness. For studies of the land surface,
however, these atmospheric effects may be unwanted [17].
Clouds and precipitation affect surface brightness measure-
ments in two ways. First, the cloud scattering nonuniformly
lowers the measured brightness temperature for all frequencies
of the SSM/I with higher frequencies progressively more
sensitive. The reduction in brightness temperature can be
confused with surface features. Second, the clouds attenuate
the polarization differences caused by the geometric or chem-
ical composition of different surface types. This prevents the
surface polarization difference from being used to discriminate
between vegetation types and/or standing water.
Fig. 1 illustrates examples of atmospherically distorted
brightness temperature images in a region of the Amazon
Basin for all vertical polarization SSM/I channels. These
images, like those in this paper, were generated by assigning
to each pixel covered by the swath the value of the nearest
measurement. Other single-pass imaging techniques can also
be used, e.g., [2] and [11]. The distortions are evident in
the temporal variation of surface brightness temperature in
different areas. Note that, as expected, the distortions are more
pronounced in the higher frequency channels. This follows the
trend of increased atmospheric scattering due to clouds and
precipitation with increasing frequency. The distortions of
pixel values can be as high as 60 K for the higher frequency
channels. These distortions can greatly hinder the application
of SSM/I data to land studies.
While multichannel- and/or multisensor-based algorithms
for cloud removal have been previously used (e.g., [4], [14],
[16], and [17]), we use a single-channel algorithm similar to
[1]. By using only single-frequency information to generate
a “cloud-free” image of the surface, we avoid introducing
spurious correlation between the channels. For example, since
each frequency has a different footprint size, using lower fre-
quency data to remove atmospheric distortion effects in higher
frequency channels may unnecessarily exclude undistorted
values in the higher frequency channels [1].
Our algorithm is based on the assumption that temporal
surface brightness variations over an area are caused by small-
scale atmospheric effects rather than temporal changes in the
surface brightness. Using multiple passes over the surface,
we generate a composite image that represents the effective
surface brightness temperature over a multiday period. The
composite image is generated from images created from each
descending pass, though ascending passes can also be used.
In the example data that follow, 20 days of descending pass
SSM/I data (September 1992) over South America are used.
During this period, each pixel is observed from five to ten
times. The value of the composite pixel is computed from this
ensemble. The study region is considered a worst-case exam-
ple, with frequent rain and distortion events occurring up to
several times during the compositing interval. For this region,
20 days offers a good balance between the number of undis-
torted measurements in the ensemble and temporal variations
due to seasonal radiometric surface response variations. This is
some what less than the 30 days used by previous investigators
[1], but it provides adequate results. Areas with less-frequent
distortion events may be able to use shorter periods.
Choudhury and Tucker [1] removed temporal atmospheric
distortion by using the second-highest pixel value from the
ensemble as the composite pixel value. Since the atmo-
spheric distortion generally lowers the brightness temperature
measurements over land, high pixel values have the least
atmospheric influence. They reason that, since the highest
value is often strongly influenced by noise or processing
artifacts, they used the second-highest pixel value.
Choosing the second-highest value is an example of a rank
order statistic [3]. Another rank order technique is the median
filter [9]. As an estimator, a rank order statistic is noise-
reducing, but it is sensitive to the underlying distribution of the
samples [13]. Thus, the second-highest value technique’s abil-
ity to reduce noise is strongly influenced by the measurement
distribution.
Unfortunately, the distribution function for the SSM/I data
is not precisely known and it is not possible to analytically
determine the estimator variance. However, it is known that
in the presence of atmospheric distortion over land, the distri-
bution is skewed low, while the desired estimation parameter
is the mode on the high end of the distribution [14]. This
strongly suggests that the rank order statistic needed for this
application is a value closer to the highest value than to the
median value. Given this insight, the second-highest value
method is a reasonable approach.
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56 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 37, NO. 1, JANUARY 1999
IV. MMA ALGORITHM
In an effort to improve the performance of a cloud-removal
algorithm, an MMA technique was developed. This algorithm
attempts to estimate the cloud-free surface brightness of a pixel
by choosing a subset of pixel values from the ensemble of
measurements of that pixel and then averaging the selected
values together. By properly selecting the subset from the
ensemble, the cloud distortion is eliminated. Averaging of the
subset reduces the noise and attenuates any residual bias.
To select pixel values from the ensemble in the MMA
technique, the sample mean of the entire pixel ensemble is first
computed. Measurements greater than the sample mean yield a
subset of the complete ensemble corresponding to its highest
values. The highest value of this subset is then eliminated.
The remaining values consist of those values that are above
the ensemble mean but less than the maximum value of the
ensemble. This is the MMA subset. The estimated pixel value
is then determined as the mean of this subset.
Analyzing this technique statistically is challenging for two
reasons: 1) the distribution of pixel values when distortions are
included is not clearly known and 2) the algorithm combines
both box averaging statistics and order statistics. To qualita-
tively justify this approach, consider a simple model for the
pixel measurements. In this model, the measurement is the
sum of a Gaussian-distributed surface brightness temperature
and a weighted binary random variable
(1)
where
is the measured brightness temperature, is the
Gaussian distribution with mean
and standard deviation ,
is a binary-valued random variable of the probability that a
measurement contains cloud distortion (less than 30% based on
a simple examination of SSM/I in the study region described
later), and
is a positive random variable representing the
drop in brightness temperature due to a cloud (
depends on
the cloud thickness, water content, etc., the statistics of which
are unknown). A schematic example of the distribution of
is
given in Fig. 2(a), where
K and %. The right
mode corresponds to the distribution of surface brightness,
while the lower mode represents the distribution of cloudy
pixels. The “X” marks below the temperature axis illustrate
an ensemble of seven random measurements for a given pixel.
Also illustrated are the results from applying the MMA and
second-highest value techniques.
Good metrics for comparing estimation algorithms include
the mean estimate error (bias) and the estimate variance.
Ideally, the estimate should have no bias and minimum vari-
ance. To compare the variances of the MMA algorithm and
the second-highest value technique, consider Fig. 2(b). As in
Fig. 2(a), the X’s represent an ensemble of seven samples
taken from the distribution. The variance of the second-highest
value technique is governed by the average temperature dif-
ference between the highest and third highest value of the
ensemble. The variance of the MMA algorithm depends on
the variances of the second, third, and fourth measurements.
Graphically, we may see that the averaging of these values
lowers the estimate variance more than just using the second-
highest value.
(a)
(b)
Fig. 2. Illustration of hypothetical radiometric measurement distribution for
a cloudy region. (a) Sample discrete ensemble. (b) Variance of the modified
maximum and second-highest value.
Like the second-highest value estimate, the MMA estimate
in this example is biased high, and it is high whenever the
ensemble includes more than one sample from the lower mode
of the mixture distribution. However, it is clear that the MMA
bias is less than the second-highest value estimate. Further,
the estimator variance is smaller for MMA.
While MMA produces a less-biased estimate for pixels with
high cloud contamination than the second-highest value, it
is still biased high for pixels with little or no contamina-
tion. Fig. 3 depicts a hypothetical distribution of brightness
temperatures for a noncloud-affected pixel. In this case, the
second-highest value and MMA estimates are biased high.
The desired value is the mean of the overall distribution in
the absence of clouds or precipitation. As previously noted, a
simple examination of SSM/I data reveals a probability of less
than 30% that a measurement is distorted by clouds or rain.
Thus, MMA may be unnecessarily biased somewhat high for
many of the measurements.
In an effort to ameliorate this problem, a hybrid of the mean
and MMA methods has been developed. Ideally, this hybrid
implements MMA in the presence of clouds and takes the
mean in their absence. This reduces the overshoot of MMA
for low atmospheric distortions and provides a better estimate
of the actual surface brightness temperature.
To implement the hybrid algorithm, a metric is required for
the decision making process. The chosen metric is the temporal
standard deviation of the values for a particular pixel. The pres-
ence of clouds skews the brightness temperature distribution
low for affected passes, thus increasing the standard deviation.
Fig. 4 shows a mean SSM/I composite image along with
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LONG et al.: CLOUD-REMOVAL ALGORITHM FOR SSM/I DATA 57
(a)
(b)
Fig. 3. Illustration of hypothetical radiometric measurement distribution for
a clear region. (a) Sample discrete ensemble. (b) Variance of the modified
maximum and second-highest value.
its corresponding temporal standard deviation image for the
85-GHz vertically polarized channel. This visually illustrates
that the standard deviation highly correlates with atmospheric
distortions. Areas that appear darkened in the mean composite
image exhibit relatively high values in the standard deviation
image.
In the standard deviation image of Fig. 4, the areas with
low values correspond to regions with little or no atmospheric
distortion. A small 2
2 spatially homogeneous subregion,
which will be more explicitly defined in a later section, is
chosen as an example of an area with low temporal variation
and thus low atmospheric distortion. The temporal mean and
standard deviation of all swath pixel values are calculated
for each vertically polarized SSM/I channel in this subregion
and shown in Table II. The standard deviations represent the
temporal variance of surface brightness temperature in the
absence of atmospheric distortion. According to the previous
discussion, any kind of temporal variation, such as atmospheric
distortion, will cause the standard deviation to rise above these
values. All channel standard deviation values are similar with
the 19-V channel exhibiting the highest and the 37-V channel
the lowest. Ideally, optimum values should be used for each
channel in implementing the hybrid algorithm. However, since
the temporal standard deviations are similar, and for the sake
of simplicity, we chose to use the highest of these values
1.25 K as the hybrid threshold metric for the results presented
in this paper. In the hybrid algorithm, the standard deviation is
computed for each pixel ensemble of brightness temperatures.
If it is above 1.25 K, the MMA algorithm is used to produce
the composite value for that particular pixel to select only
nondistorted measurements. Otherwise, the mean is used. We
note that this threshold has been chosen for use in the study
region and should be tuned for other regions.
V. S
IMULATION
To further compare and contrast the mean, second-highest
value, MMA, and hybrid algorithms, a simple Monte Carlo
analysis for a single pixel is presented. This simulation as-
sumes that the true pixel brightness for a geographical area is
280 K. An ensemble of seven pixel values is then created by
adding a Gaussian random variable of standard deviation 1 K
to the “true” value. This represents the radiometric “noise”
inherent to the radiometer measurements. Seven pixels
simulate the average number of radiometric measurements in
the 20-day study period. Two of the ensemble measurements
then have simulated atmospheric distortion added. The first
measurement is reduced by
and the second measurement
by
2. This models a pixel that is contaminated by clouds
at two different times with one cloud twice as distorting as
the other. The seven-member ensemble is then processed by
each algorithm, and the results are saved. The results of 1000
simulations are then analyzed to give the results in Fig. 5.
For comparison, the ensemble mean is plotted along with the
windowed mean. The windowed mean is the mean of values
within one standard deviation of the ensemble mean.
For pixels with little or no atmospheric distortion, the mean
or windowed average is closer to the 280 “true” value than
MMA or the second-highest value. For ensembles that have
greater (
5 K) atmospheric distortion, the second-highest
value and MMA techniques are superior. The MMA tech-
nique has the smallest bias of the two. Since it also has the
smallest variance, the MMA algorithm is considered superior
to the second-highest value algorithm. The hybrid algorithm
combines the strengths of the mean method for low distortion
temperatures and MMA for high distortion temperatures. This
is evident in Fig. 5 by the closer estimates to 280 K for small
. The simulation results indicate that MMA is superior
in the presence of significant distortion and mean is best
with little or no distortion present, while the hybrid algorithm
combines the two in a manner that uses the appropriate
algorithm for each pixel.
VI. SSM/I D
ATA ANALYSIS
To validate the algorithms with actual data, a region of
the Amazon Basin was chosen for SSM/I data analysis. The
region lies primarily within Brazil and is bounded by the
coordinates: 48–63
W longitude and 1–16 S latitude. Its
characteristic high precipitation levels make it a good study
region, representing a worst-case scenario with frequent rain
and distortion events. The mean, second-highest value, MMA,
and hybrid composite images of this region were created for all
vertically polarized SSM/I channels. Examples are presented
in Figs. 6 and 7. In the interest of space, only the 37- and
85-V images are shown here. Due to smaller 3-dB antenna
footprints, the higher frequency images exhibit better effective
resolution.
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Figures (14)
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Journal ArticleDOI
TL;DR: The proposed methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance.
Abstract: The frequent presence of clouds in passive remotely sensed imagery severely limits its regular exploitation in various application fields. Thus, the removal of cloud cover from this imagery represents an important preprocessing task consisting in the reconstruction of cloud-contaminated data. The intent of this study is to propose two novel general methods for the reconstruction of areas obscured by clouds in a sequence of multitemporal multispectral images. Given a cloud-contaminated image of the sequence, each area of missing measurements is reconstructed through an unsupervised contextual prediction process that reproduces the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. In the first method, the contextual prediction process is implemented by means of an ensemble of linear predictors, each trained over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected subset. In order to obtain such regions, each temporal image is locally classified by an unsupervised classifier based on the expectation-maximization (EM) algorithm. In the second method, the local spectro-temporal relationships are reproduced by a single nonlinear predictor based on the support vector machines (SVM) approach. To illustrate the performance of the two proposed methods, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 Enhanced Thematic Mapper Plus sensor over a total period of four months is reported and discussed. It includes a detailed simulation study that aims at assessing with different reconstruction quality criteria the accuracy of the methods in different qualitative and quantitative cloud contamination conditions. Compared with two techniques based on compositing algorithms for cloud removal, the proposed methods show a clear superiority, which makes them a promising and useful tool in solving the considered problem, whose great complexity is commensurate with its practical importance.

108 citations


Cites background or methods from "A cloud-removal algorithm for SSM/I..."

  • ...Selection of the MMA subset of measurements is carried out by considering all the measurements above the vector mean except the one with the highest value....

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  • ...In [25], the authors show that a hybrid algorithm that implements MMA in the presence of clouds and averages the measurements in their absence, can significantly improve the quality of the composite image compared to the MMA and the SH algorithms....

    [...]

  • ...For the purpose of comparison, we implemented two other methods based on compositing algorithms developed to reconstruct cloudy areas in images acquired by the microwave SSM/I sensor [25]....

    [...]

  • ...Several algorithms, like the second highest (SH) [24] and the modified maximum average (MMA) [25], have been developed to remove cloud effects from SSM/I images....

    [...]


Journal ArticleDOI
TL;DR: An efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented, based on the Bandelet transform and the multiscale geometrical grouping.
Abstract: It is well known that removing cloud-contaminated portions of a remotely sensed image and then filling in the missing data represent an important photo editing cumbersome task. In this paper, an efficient inpainting technique for the reconstruction of areas obscured by clouds or cloud shadows in remotely sensed images is presented. This technique is based on the Bandelet transform and the multiscale geometrical grouping. It consists of two steps. In the first step, the curves of geometric flow of different zones of the image are determined by using the Bandelet transform with multiscale grouping. This step allows an efficient representation of the multiscale geometry of the image's structures. Having well represented this geometry, the information inside the cloud-contaminated zone is synthesized by propagating the geometrical flow curves inside that zone. This step is accomplished by minimizing a functional whose role is to reconstruct the missing or cloud contaminated zone independently of the size and topology of the inpainting domain. The proposed technique is illustrated with some examples on processing aerial images. The obtained results are compared with those obtained by other clouds removal techniques.

97 citations


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  • ...[5] for recovering Advanced Very High Resolution Radiometer measurements that are modified by the effects not only of clouds but also of cloud shadows....

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  • ...Each of the obscured region is reconstructed using our bandelet-based method as well as two other multitemporal analysis methods, namely, the modified maximum average [5] and the nearest neighbor method....

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Journal ArticleDOI
TL;DR: This letter presents a postreconstruction methodology for improving the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image and proposes a solution to a problem that has not been addressed in the remote sensing literature, i.e., the generation of an error map beside the reconstructed images to provide end-users with helpful indications about reconstruction reliability.
Abstract: A general method has been proposed recently for the contextual reconstruction of cloud-contaminated areas in multitemporal multispectral images. It is based on the idea of making the prediction process learn from information available in the cloud-free neighborhood of contaminated areas. Though promising, this method does not fully exploit all available information, thus leaving room for further methodological enhancements. This letter presents a postreconstruction methodology for improving the contextual reconstruction process by opportunely capturing spatial and spectral correlations characterizing the considered image. In addition, we propose a solution to a problem that has not yet been addressed in the remote sensing literature, i.e., the generation of an error map beside the reconstructed images to provide end-users with helpful indications about reconstruction reliability. Thorough experiments conducted on a multitemporal sequence of Landsat-7 ETM+ images are reported and discussed.

48 citations


Cites methods from "A cloud-removal algorithm for SSM/I..."

  • ...Among the relatively few works available in the cloud removal literature, one can find the compositing approaches [3], a cloud-specific retrieval algorithm [4], a method based on regression trees and histogram matching [5], and an ecosystem classification-dependent temporal interpolation technique [6]....

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Journal ArticleDOI
TL;DR: The temporal signature from each of these sensors was found to complement each other in crop growth monitoring and the decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover.
Abstract: Estimation of crop area, growth and phenological information is very important for monitoring of agricultural crops. However, judicious combination of spatial and temporal data from different spectral regions is necessary to meet the requirement. This study highlights the use of active microwave QuikSCAT Ku-band scatterometer and Special Sensor Microwave/Imager (SSM/I) passive microwave radiometer data to derive information on important phenological phases of rice crop. The wetness index, a weekly composite product derived using brightness temperatures from 19, 37 and 85 GHz channels of SSM/I, was used to identify the puddling period. Ku-band scatterometer data provided the signal of transplanted rice seedlings since they acts as scatterers and increases the backscattering. Dual peak nature of temporal backscatter curve around the heading stage of rice crop was observed in Ku-band. The decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover. The symmetric (Gaussian) and asymmetric (lognormal) curve fits were attempted to derive the date of initiation of the heading phase. The temporal signature from each of these sensors was found to complement each other in crop growth monitoring. Image showing pixel-wise timings of heading stage revealed the differences exists in various parts of the study area.

29 citations


Cites background from "A cloud-removal algorithm for SSM/I..."

  • ...The algorithm for the generation of the composite brightness temperature product is discussed in Long et al. (1999)....

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Journal ArticleDOI
TL;DR: Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels.
Abstract: Cloud contamination is a big obstacle when processing satellite images retrieved from visible and infrared spectral ranges for application. Although computational techniques including interpolation and substitution have been applied to recover missing information caused by cloud contamination, these algorithms are subject to many limitations. In this paper, a novel smart information reconstruction (SMIR) method is proposed, in order to reconstruct cloud contaminated pixel values from the time-space-spectrum continuum with the aid of a machine learning tool, namely extreme learning machine (ELM). For the purpose of demonstration, the performance of SMIR is evaluated by reconstructing the missing remote sensing reflectance values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra satellite over Lake Nicaragua, where is a very cloudy area year round. For comparison, the traditional backpropagation neural network algorithms will also be implemented to reconstruct the missing values. Experimental results show that the ELM outperforms the BP algorithms by an enhanced machine learning capacity with simulated memory effect embedded in MODIS due to linking the complex time-space-spectrum continuum between cloud-free and cloudy pixels. The ELM-based SMIR practice presents a correlation coefficient of 0.88 with root mean squared error of $7.4{\hbox{E}} - 04{\hbox{sr}}^{-1}$ between simulated and observed reflectance values. Finding suggests that the SMIR method is effective to reconstruct all the missing information providing visually logical and quantitatively assured images for further image processing and interpretation in environmental applications.

27 citations


Cites background or methods from "A cloud-removal algorithm for SSM/I..."

  • ...lected from previous time periods with spatial relationships, and are mainly applied to the homogeneous landscapes with less dynamic nature [6]–[9]....

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  • ...Other possible methods, such as the second highest value [48] and the modified maximum average [6], could be utilized to optimize the multiple predicted results for possible...

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References
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03 Oct 1988
TL;DR: This chapter discusses two Dimensional Systems and Mathematical Preliminaries and their applications in Image Analysis and Computer Vision, as well as image reconstruction from Projections and image enhancement.
Abstract: Introduction. 1. Two Dimensional Systems and Mathematical Preliminaries. 2. Image Perception. 3. Image Sampling and Quantization. 4. Image Transforms. 5. Image Representation by Stochastic Models. 6. Image Enhancement. 7. Image Filtering and Restoration. 8. Image Analysis and Computer Vision. 9. Image Reconstruction From Projections. 10. Image Data Compression.

8,403 citations


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01 Aug 1982
Abstract: EN BIBLIOTECA: V.1: MICROWAVE REMOTE SENSING FUNDAMENTALS AND RADIOMETRY. V.2: RADAR REMOTE SENSING AND SURFACE SCATTERING AND EMISSION THEORY

3,426 citations


Book
01 Dec 1971
TL;DR: Theoretical Bases for Calculating the ARE Examples of the Calculations of Efficacy and ARE Analysis of Count Data.
Abstract: Introduction and Fundamentals Introduction Fundamental Statistical Concepts Order Statistics, Quantiles, and Coverages Introduction Quantile Function Empirical Distribution Function Statistical Properties of Order Statistics Probability-Integral Transformation Joint Distribution of Order Statistics Distributions of the Median and Range Exact Moments of Order Statistics Large-Sample Approximations to the Moments of Order Statistics Asymptotic Distribution of Order Statistics Tolerance Limits for Distributions and Coverages Tests of Randomness Introduction Tests Based on the Total Number of Runs Tests Based on the Length of the Longest Run Runs Up and Down A Test Based on Ranks Tests of Goodness of Fit Introduction The Chi-Square Goodness-of-Fit Test The Kolmogorov-Smirnov One-Sample Statistic Applications of the Kolmogorov-Smirnov One-Sample Statistics Lilliefors's Test for Normality Lilliefors's Test for the Exponential Distribution Anderson-Darling Test Visual Analysis of Goodness of Fit One-Sample and Paired-Sample Procedures Introduction Confidence Interval for a Population Quantile Hypothesis Testing for a Population Quantile The Sign Test and Confidence Interval for the Median Rank-Order Statistics Treatment of Ties in Rank Tests The Wilcoxon Signed-Rank Test and Confidence Interval The General Two-Sample Problem Introduction The Wald-Wolfowitz Runs Test The Kolmogorov-Smirnov Two-Sample Test The Median Test The Control Median Test The Mann-Whitney U Test and Confidence Interval Linear Rank Statistics and the General Two-Sample Problem Introduction Definition of Linear Rank Statistics Distribution Properties of Linear Rank Statistics Usefulness in Inference Linear Rank Tests for the Location Problem Introduction The Wilcoxon Rank-Sum Test and Confidence Interval Other Location Tests Linear Rank Tests for the Scale Problem Introduction The Mood Test The Freund-Ansari-Bradley-David-Barton Tests The Siegel-Tukey Test The Klotz Normal-Scores Test The Percentile Modified Rank Tests for Scale The Sukhatme Test Confidence-Interval Procedures Other Tests for the Scale Problem Applications Tests of the Equality of k Independent Samples Introduction Extension of the Median Test Extension of the Control Median Test The Kruskal-Wallis One-Way ANOVA Test and Multiple Comparisons Other Rank-Test Statistics Tests against Ordered Alternatives Comparisons with a Control Measures of Association for Bivariate Samples Introduction: Definition of Measures of Association in a Bivariate Population Kendall's Tau Coefficient Spearman's Coefficient of Rank Correlation The Relations between R and T E(R), tau, and rho Another Measure of Association Applications Measures of Association in Multiple Classifications Introduction Friedman's Two-Way Analysis of Variance by Ranks in a k x n Table and Multiple Comparisons Page's Test for Ordered Alternatives The Coefficient of Concordance for k Sets of Rankings of n Objects The Coefficient of Concordance for k Sets of Incomplete Rankings Kendall's Tau Coefficient for Partial Correlation Asymptotic Relative Efficiency Introduction Theoretical Bases for Calculating the ARE Examples of the Calculations of Efficacy and ARE Analysis of Count Data Introduction Contingency Tables Some Special Results for k x 2 Contingency Tables Fisher's Exact Test McNemar's Test Analysis of Multinomial Data Summary Appendix of Tables Answers to Problems References Index A Summary and Problems appear at the end of each chapter.

2,863 citations


Journal ArticleDOI
Abstract: The subject of this study is the identification of precipitation in warm and cold land and ocean environments from the Defense Meteorological Satellite Program's (DMSP) Special Sensor Micmwave/Imager (SSM/I). The high sensitivity of the SSM/I 85.5 GHz channels to volume scattering by precipitation, especially ice above the freezing level, is the basis for this identification. This ice scattering process causes SSM/I 85.5 GHz brightness temperatures to occasionally fall below 100 K. It is demonstrated that the polarization diversity available at 85.5 GHz from the SSM/I allows discrimination between low brightness temperatures due to surface water bodies versus those due to precipitation. An 85.5 GHz polarization corrected temperature (PCT) is formulated to isolate the precipitation effect. A PCT threshold of 255 K is suggested for the delineation of precipitation. This threshold is shown to be lower than what would generally be expected from nonprecipitating cloud water alone, yet high enough to s...

599 citations


Journal ArticleDOI
TL;DR: The results of this effort demonstrate that the SSM/I is a stable, sensitive, and well-calibrated microwave radiometric system capable of providing accurate brightness temperatures for microwave images of the Earth and for use by environmental product retrieval algorithms.
Abstract: The Special Sensor Microwave/Imager (SSM/I) instrument and scan geometry are briefly described. The results of investigations of the stability of the gain, calibration targets and spin rate, the radiometer noise and sensitivity, the coregistration, the beam width and main-beam efficiency of the antenna beams, and the absolute calibration and geolocation of the instrument are presented. The results of this effort demonstrate that the SSM/I is a stable, sensitive, and well-calibrated microwave radiometric system capable of providing accurate brightness temperatures for microwave images of the Earth and for use by environmental product retrieval algorithms. It is predicted that this SSM/I and the 11 future ones currently built or to be built will provide high-performance microwave measurements for determination of global weather and critical atmospheric, oceanographic, and land parameters to operational forecasters and users and the research community for the next two decades. >

561 citations


Frequently Asked Questions (1)
Q1. What contributions have the authors mentioned in the paper "A cloud-removal algorithm for ssm/i data" ?

In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager ( SSM/I ) data with the aid of new algorithms to eliminate smallscale distortion caused by clouds or precipitation.