Journal ArticleDOI

A cloud-removal algorithm for SSM/I data

01 Jan 1999--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.

Topics: Distortion (57%), Hybrid algorithm (54%)

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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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
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
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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
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
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

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]....

[...]

• ...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...

[...]

References
More filters

Book
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

Book
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.

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