scispace - formally typeset

Journal ArticleDOI

A comparison of AVIRIS and Landsat for land use classification at the urban fringe

01 Jul 2004-Photogrammetric Engineering and Remote Sensing (American Society for Photogrammetry and Remote Sensing)-Vol. 70, Iss: 7, pp 813-819

AbstractIn this study we tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. After processing the AVIRIS image and creating a synthetic Landsat image, we used standard classification and post-classification procedures to compare the data sources for land use mapping. We found that, for this location, AVIRIS holds modest, but real, advantages over Landsat for the classification of heterogeneous and vegetated land uses. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR).

Summary (2 min read)

Introduction

  • In rapidly urbanizing areas, such as the Front Range of Colorado, maps fast lose their validity.
  • Less traditional sensors may provide additional information that can improve mapping accuracy.
  • The Airborne Visible Infrared Imaging Spectrometer , for example, produces images with 224 spectral bands between 0.4 and 2.45 m, compared to six bands for Landsat (not including the thermal band) and three for SPOT’s Multispectral Imager (XS).

Sensor Specifications and Classification Accuracy: The Case of the Urban Fringe

  • Among the factors that may influence classification accuracy are the Ground Sampling Distance (GSD), number of spectral bands, and Signal-to-Noise Ratio (SNR) of a sensor.
  • Generally, it is thought that GSD is the most important factor for classification accuracy of built environments (Forster, 1985).
  • SNR, which varies sensor-by-sensor and band-by-band and pixel-by-pixel, may also influence classification accuracy.
  • Finally, the number of spectral bands may influence accuracy of land use classification.

Image Processing

  • An AVIRIS flight line was acquired for 30 September 1999 along the northern Front Range of Colorado.
  • Page 814 takes advantage of recent advancements in atmospheric radiative transfer, resulting in highly accurate atmospheric corrections (Qu et al., 2000; Qu et al., 2002).
  • An AVIRIS image was compared to synthetic Landsat ETM+ image derived from AVIRIS.
  • To estimate the noise levels of ETM+, the following model was used (John Barker, NASA/Goddard Space Flight Center, personal communication): SNR DN (a b * DN )^.5, (1) where DN is the digital number of a pixel, and a and b are coefficients for each band calibrated on ETM+ data from 06 September 2002.
  • A MNF transform, similar to a principal components transform, derives a series of uncorrelated bands and segregates noise in the data.

Classification Methodology

  • There are myriad classification methods, each with different properties.
  • Unsupervised classification automatically separates land use into a number of computer-defined categories.
  • Linear spectral mixing derives pixel-by-pixel measures of abundance for spectrally pure materials.
  • Using ENVI, images were classified into eight classes with the ML classifier.
  • The ground truth image was geometrically registered to the AVIRIS image using a 1-degree polynomial and bilinear resampling with 20 ground control points.

Results

  • Accuracy of a supervised classification of land use typically ranges between 60 percent–90 percent depending on the classification scheme, the classifier, and the image itself.
  • The accuracy assessment verified that the AVIRIS classification was superior to that of the synthetic ETM+ image.
  • Importantly, the results of the synthetic ETM+ classification were virtually identical to a similar classification of ETM+ without added noise.
  • On the off-diagonal, numbers show the change in misclassification; a negative number indicates that the classification does not confuse these classes as often with AVIRIS data.
  • Page 817 versus vegetated land uses; commercial/industrial versus fallow, dry rangeland, and residential; and, urban irrigation versus irrigated crops and grassland.

Discussion

  • The classified images contained similar types of misclassifications, often due to the well-identified problem of heterogeneity in urban land covers (Forster, 1985).
  • Residential areas were sometimes confused with vegetated land uses because both have mixtures of soil and vegetation.
  • The false positives decreased, in some cases dramatically, again perhaps because subtle signatures in the spectrum distinguished easily confused classes.
  • The producer’s accuracy for fallow, water, and dry rangeland decreased with AVIRIS.
  • In these fairly homogenous land uses, perhaps the additional bands in AVIRIS simply added noise and provided no additional useful information over synthetic Landsat.

Conclusion

  • A supervised classification of AVIRIS was more accurate than one of synthetic Landsat ETM+ for land use classification at the urban fringe.
  • Which imagery a researcher should use, provided both are available, largely depends on the purpose of the study.
  • On the other hand, using AVIRIS produced a greater number of false positives for commercial/industrial land and performed poorly in classifications of relatively homogenous, less-vegetated land uses, such as fallow and dry rangeland.
  • Since classification accuracy is dependent on a number of factors besides sensor specifications, caution should be used in extending the conclusions of this study to other areas.

Did you find this useful? Give us your feedback

...read more

Content maybe subject to copyright    Report

3:/6432+38'1"89*/+7')918< 9(1/)'8/437 3:/6432+38'1"89*/+7

A Comparison of AVIRIS and Landsat for Land
Use Classi#cation at the Urban Fringe
Rutherford V. Pla$
Geysburg College
Alexander F.H. Goetz
4114;8./7'3*'**/8/43'1;4607'8 .B57)9541'-+B<7(96-+*9+7,')
'684,8.+ 3:/6432+38'13*/)'8467'3*25')877+772+38422437
Share feedback about the accessibility of this item.
?/7/78.+59(1/7.+67:+67/434,8.+;460?/759(1/)'8/43'55+'67/3+B<7(96-411+-+7/378/898/43'16+547/846<(<5+62/77/434,
8.+)45<6/-.84;3+6,465+6743'197+348,466+*/786/(98/439541'5+62'3+381/30.B57)9541'-+B<7(96-+*9+7,')
?/745+3'))+77'68/)1+/7(649-.884<49(<?+9541'").41'67./5'8+B<7(96-411+-+8.'7(++3'))+58+*,46/3)197/43(<'3
'98.46/=+*'*2/3/786'8464,?+9541'46246+/3,462'8/4351+'7+)438')8 )9541'-+B<7(96-+*9
1'B!%4+8=425'6/7434,%!"'3*'3*7'8,46'3*$7+1'77/@)'8/43'88.+$6('36/3-+
.484-6'22+86/)3-/3++6/3-!+248+"+37/3-

A Comparison of AVIRIS and Landsat for Land Use Classi#cation at the
Urban Fringe
Abstract
38./7789*<;+8+78+*;.+8.+6%!"*'8''114;+*,46/2564:+*1'3*97+)1'77/@)'8/434:+67<38.+8/)
'3*7'8#*'8',46'14)'8/43438.+96('3696'1,6/3-+4,4146'*4A+6564)+77/3-8.+%!"/2'-+
'3*)6+'8/3-'7<38.+8/)'3*7'8/2'-+;+97+*78'3*'6*)1'77/@)'8/43'3*5478)1'77/@)'8/43564)+*96+784
)425'6+8.+*'8'7496)+7,461'3*97+2'55/3-&+,493*8.'8,468./714)'8/43%!".41*724*+78(98
6+'1'*:'38'-+74:+6'3*7'8,468.+)1'77/@)'8/434,.+8+64-+3+497'3*:+-+8'8+*1'3*97+7968.+6246+
8./7'*:'38'-+)42+7'12478+38/6+1<,6428.+1'6-+392(+64,7+374675+)86'1('3*76'8.+68.'38.+./-.
"/-3'1844/7+!'8/4"!
Keywords
%!"'3*78'8#4146'*4$6('36/3-+"/-3'1844/7+!'8/4
Disciplines
3:/6432+38'13*/)'8467'3*25')877+772+38>3:/6432+38'1")/+3)+7
?/7'68/)1+/7':'/1'(1+'8?+9541'").41'67./5'8+B<7(96-411+-+ .B57)9541'-+B<7(96-+*9+7,')

Abstract
In this study we tested whether AVIRIS data allowed for im-
proved land use classification over synthetic Landsat
ETM+
data for a location on the urban-rural fringe of Colorado. After
processing the
AVIRIS image and creating a synthetic Landsat
image, we used standard classification and post-classification
procedures to compare the data sources for land use mapping.
We found that, for this location,
AVIRIS holds modest, but real,
advantages over Landsat for the classification of heteroge-
neous and vegetated land uses. Furthermore, this advantage
comes almost entirely from the large number of sensor spec-
tral bands rather than the high Signal-to-Noise Ratio (
SNR).
Introduction
In rapidly urbanizing areas, such as the Front Range of Col-
orado, maps fast lose their validity. Large areas of prairie or
farmland land can be overrun by residential development in a
matter of months. Remotely sensed data allows land use and
land cover to be mapped quickly, relatively cheaply, and fre-
quently. With improved mapping of rapidly changing areas,
planners will be able to better address issues associated with
urban sprawl. However, the choice of sensor can significantly
influence the accuracy of the classification. While it is com-
monly thought that smaller Ground Sampling Distance (
GSD),
also called pixel size, is the key to better land use classifica-
tion, the number of spectral bands and the Signal-to-Noise
Ratio (
SNR) may influence classification accuracy as well.
Commonly, researchers use sensors such as those on
Landsat or
SPOT satellites for mapping land use and land cover
(Table 1). Of these, the Landsat sensors have more spectral
bands and a longer time series, while
SPOT provides smaller
GSD. Less traditional sensors may provide additional informa-
tion that can improve mapping accuracy. The Airborne Visible
Infrared Imaging Spectrometer (
AVIRIS), for example, produces
images with 224 spectral bands between 0.4 and 2.45 m,
compared to six bands for Landsat (not including the thermal
band) and three for
SPOT’s Multispectral Imager (XS). Sensors
with a large number of continuous spectral bands, such as
AVIRIS, are called hyperspectral imagers (Green et al., 1998).
Though hyperspectral imagers have been used in studies
of mineralogical mapping and ecology, they have rarely been
employed for land use mapping. A small number of studies
have explored the integration of hyperspectral and Synthetic
Aperture Radar (
SAR) for urban mapping (Gamba and Housh-
A Comparison of AVIRIS and Landsat for
Land Use Classification at the Urban Fringe
Rutherford V. Platt and Alexander F.H. Goetz
mand, 2001; Hepner et al., 1998). Other studies have used
hyperspectral imagery to map a narrow range of urban materi-
als and processes (Ben-Dor et al., 2001; Ridd et al., 1992; Salu,
1995). One study used an iterative spectral un-mixing proce-
dure to delineate urban materials (Roessner et al., 2001). To
date, however, no studies have tested whether hyperspectral
imagery improves land use classification accuracy over and
above multispectral imagery such as from Landsat.
In this study, we tested whether
AVIRIS data allowed for
improved land use classification over synthetic Landsat ETM+
data for a location on the urban-rural fringe of Colorado. We
expected that the large number of bands and high
SNR pro-
vided by AVIRIS would help distinguish land cover types that
are easily confused (irrigated urban areas and irrigated crops,
for example). After processing the
AVIRIS image and creating a
synthetic Landsat image, we used standard classification and
post-classification procedures to compare the data sources for
land use mapping.
Sensor Specifications and Classification Accuracy: The Case
of the Urban Fringe
Among the factors that may influence classification accuracy
are the Ground Sampling Distance (
GSD), number of spectral
bands, and Signal-to-Noise Ratio (
SNR) of a sensor. Generally, it
is thought that
GSD is the most important factor for classification
accuracy of built environments (Forster, 1985). For example, a
study in Indonesia found that
SPOT Multispectral (XS) images are
superior to Landsat Multispectral Scanner (
MSS) images for
mapping of heterogeneous, near-urban land cover because of
SPOT’s smaller pixel size (Gastellu-Etchegorry, 1990). The link
between
GSD and classification accuracy, however, is sometimes
tenuous. In heterogeneous areas, such as residential areas, it has
been shown that classification accuracies may actually improve
by up to 20 percent as
GSD is increased (Cushnie, 1987). This oc-
curs when the reflectance spectra of a variety of cover types in
an urban environment blend to form an overall urban signal that
can be easily distinguished from other land covers.
SNR, which varies sensor-by-sensor and band-by-band and
pixel-by-pixel, may also influence classification accuracy. The
greater the
SNR, the more usable information is available in
the data. Overall,
AVIRIS has much higher SNR than Landsat
sensors. Within the Landsat family, the
ETM+ in Landsat 7 has
a higher
SNR than the Thematic Mapper (TM) in Landsat 4 and
5.
SNR may vary depending not only on sensor characteristics
but also on the signal strength; summer images will have a
higher
SNR than winter images for the same time and place.
While the advantages of high
SNR are well documented in do-
mains such as mineralogical mapping (Chabrillat et al., 2002;
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2004
813
R.V. Platt was formerly with the Department of Geography,
UCB 260, University of Colorado at Boulder, Boulder, CO
80309. He is presently with the Department of Environmental
Studies, Gettysburg College, Gettysburg, PA 17325
(rplatt@gettysburg.edu).
A.F.H. Goetz is with the Center for the Study of Earth from
Space/CIRES/Department of Geological Sciences, UCB 216,
University of Colorado, Boulder, CO 80309
(goetz@cses.colorado.edu).
Photogrammetric Engineering & Remote Sensing
Vol. 70, No. 7, July 2004, pp. 813–819.
0099-1112/04/7007–0813/$3.00/0
© 2004 American Society for Photogrammetry
and Remote Sensing
99-018.qxd 6/9/04 10:51 AM Page 813

Smailbegovic et al., 2000), they have not been thoroughly as-
sessed for land use mapping. It is likely that the influence of
SNR on classification accuracy depends heavily on the classes
of interest. For example, distinguishing irrigated urban land
from irrigated cropland may require a higher
SNR than would
be needed to distinguish spectrally disparate land uses such
as residential land and fallow land.
Finally, the number of spectral bands may influence accu-
racy of land use classification. One study showed the benefits
of increasing the number of bands in classification of the
urban fringe. The study used
SPOT XS data to map farmland
and urban land uses in New Zealand (Gao and Skillcorn,
1998). In this case, using multispectral imagery improved the
delineation of urban areas and farmland because vegetative
land covers were easier to discriminate with a near-infrared
band. In cases where different land uses have similar but sep-
arable spectra, increasing the number of spectral bands will
likely improve mapping accuracy. When land uses are either
spectrally inseparable or clearly distinct, however, additional
bands may not improve classification accuracy. In these cases
the extra bands could add noise and spectral heterogeneity,
resulting in lower classification accuracy.
These studies show that decreasing
GDR, increasing SNR,
and increasing the number of bands may improve classifica-
tion accuracy for land use mapping, but the net benefits often
depend on the particular scene and classification system. In
this study
AVIRIS data was compared with synthetic Landsat
ETM+, fixed at 20 meter spatial resolution to determine the
possible effects of increased number of bands and higher
SNR
for land use mapping at the urban fringe in Colorado.
Image Processing
An AVIRIS flight line was acquired for 30 September 1999
along the northern Front Range of Colorado. A single image
cube was extracted that encompassed much of Fort Collins
along with the surrounding agricultural land and Horsetooth
Reservoir (Figure 1).
In order to convert at-sensor radiance into surface re-
flectance, an atmospheric correction was performed with
High-Accuracy Atmosphere Correction for Hyperspectral Data
(
HATCH). Using spectral features within the data, HATCH creates
pixel-by-pixel estimates of atmospheric composition.
HATCH
814
July 2004
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
TABLE 1. SENSOR CHARACTERISTICS
Landsat
AVIRIS TM/ETM+ SPOT XS
Platform Airborne Spaceborne Spaceborne
Ground Sampling Distance 20 m 30 m 20 m
Number of Bands 224 6 3
(excluding thermal)
Signal-to-Noise Ratio High Moderate Moderate
Launch 1987 1982 1986
Figure 1. Synthetic Landsat ETM+ Band 3 image of Fort Collins and surroundings.
99-018.qxd 6/9/04 10:51 AM Page 814

takes advantage of recent advancements in atmospheric radia-
tive transfer, resulting in highly accurate atmospheric correc-
tions (Qu et al., 2000; Qu et al., 2002). After conducting the
atmospheric correction, bands in the major water absorption
features at 1.4 m and 1.9 m were removed.
In this study, an
AVIRIS image was compared to synthetic
Landsat
ETM+ image derived from AVIRIS. This method elimi-
nated several sources of error that would be present if a real
Landsat image were used. First,
AVIRIS images from 1999 and
earlier contain unsystematic distortions introduced by the
pitch, yaw, and roll of the aircraft (currently a three-axis gyro-
scope is attached to the sensor and records these movements
so that the distortions may later be removed from the images).
As a result, some
AVIRIS images may be difficult to register to
other images with high precision. Secondly, the
GSD of AVIRIS
(20 meters) is finer than that of Landsat (30 meters), necessi-
tating a resampling procedure that would degrade and possi-
bly introduce additional distortions to the image. Finally, the
two images would be recorded at different times of the day, on
different days, with different atmospheric conditions that
would need to be corrected. Though it is likely that the cumu-
lative effects of these differences would be small, they would
no doubt introduce errors into the comparison.
A solution to all of these issues was to create an image
from
AVIRIS that closely matches the output of Landsat ETM+.
This was done with a two-step process. In the first step, the
appropriate
AVIRIS bands were combined to approximate the
following Landsat bands:
Band 1: 0.45–0.52 m (blue)
Band 2: 0.52–0.60 m (green)
Band 3: 0.63–0.69 m (red)
Band 4: 0.76–0.90 m (near infrared)
Band 5: 1.55–1.75 m (short wave infrared)
Band 7: 2.08–2.35 m (short wave infrared)
To create each synthetic
ETM+ band, 7 to 27 AVIRIS bands
were combined. Since each detector is most sensitive to the
wavelength at the center of the sensor bandwidth, the
AVIRIS
bands that fell in the middle of a Landsat band were weighted
more than those that fell toward the edge of the band, accord-
ing to the Landsat
ETM+ filter response function. Before pro-
ceeding, the dynamic range of the synthetic Landsat images
was degraded from 12 bits to 8 bits to approximate
ETM+.
In the second step, the
SNR of the synthetic ETM+ image
was degraded to approximate the SNR present in actual ETM+. In
1999, when the image was taken,
AVIRIS bands had an SNR as
high as approximately 1000 (figures from Robert O. Green, Jet
Propulsion Laboratory, personal communication). Since noise
is inversely proportional to the square root of the number of
bands, the synthetic
ETM+ has even lower noise than actual
AVIRIS
data and is approximately 28 to 37 times greater per band
than that of
ETM+(Table 2). As a result, AVIRIS may outperform
ETM+ even if spatial and spectral resolution were equalized.
To estimate the noise levels of ETM+, the following model
was used (John Barker, NASA/Goddard Space Flight Center,
personal communication):
SNR DN(a b
*
DN )^.5, (1)
where DN is the digital number of a pixel, and a and b are
coefficients for each band calibrated on
ETM+ data from 06 Sep-
tember 2002. The model produced estimated per-pixel
ETM+
SNR (scene averages shown in Table 2). Dividing the DNsby
the estimated
SNR produced an estimated noise level for each
pixel. Gaussian noise images were then created with a stan-
dard deviation equal to this noise level (over and above that of
AVIRIS) for each pixel of each band. These noise images were
added to each synthetic
ETM+ band to approximate the noise in
the actual
ETM+ sensors. The resulting synthetic ETM+ images
very closely approximated the bands and
SNR of actual Landsat
ETM+, only with a GSD of 20 meters instead of 30 meters.
After creating the synthetic Landsat image, a Maximum
Noise Fraction (
MNF) transform was performed on the AVIRIS
cube and synthetic Landsat images to reduce processing time
and noise, (Green et al., 1988. Note:
MNF is referred to as
“Minimum Noise Fraction” in Environment for Visualizing
Images (
ENVI) image processing software). A MNF transform,
similar to a principal components transform, derives a series
of uncorrelated bands and segregates noise in the data. Unlike
a principal components transform, a
MNF transform equalizes
the noise across bands so that image data with variance lower
than noise is not hidden in higher bands. All
MNF bands with
an eigenvalue of less than two were eliminated since these
bands contain mostly noise. The number of remaining bands
equals the dimensionality of the image. In this case, the syn-
thetic
ETM+ data had a dimensionality of five, and the AVIRIS
data had a dimensionality of 30. All subsequent analysis was
conducted on these two data sets.
Figures 2 and 3 show the band loadings for
MNF bands 1
to 5 and 16 to 30. There are 210
AVIRIS bands between 0.4 and
2.5 m. The first few
MNF bands (Figure 2) show loadings that
peak in the atmospheric windows and are not single-wave-
length specific. In fact, the peak loadings fall approximately in
line with the Landsat bands. On the other hand, in the higher
MNF bands (Figure 3) the loadings are much more wavelength
specific as evidenced by the sharp changes throughout the
spectrum. Some of these significant loadings are associated
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
July 2004
815
T
ABLE 2. SIGNAL TO NOISE RATIO (SNR) OF SYNTHETIC AND TRUE LANDSAT ETM+
Average
SNR for
AVIRIS
bands in SNR of Estimated Ratio of
ETM+ synthetic synthetic SNR of synthetic to
Band ETM+ ETM+ true ETM+ true SNR
1 912 2412 87 28
2 1033 2923 96 31
3 982 2778 75 37
4 821 3178 147 22
5 584 2611 102 26
7 377 1958 67 29
Figure 2. Band loadings for MNF 1-5. Note that peak load-
ings of the AVIRIS data roughly correspond to Landsat
bands.
99-018.qxd 6/9/04 10:51 AM Page 815

Citations
More filters

Journal ArticleDOI
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

2,280 citations


Cites background or methods from "A comparison of AVIRIS and Landsat ..."

  • ...…Erikson 2004 Hyperspectral data AVIRIS Benediktsson et al. 1995, Jimenez et al. 1999, Okin et al. 2001, Kokalya et al. 2003, Segl et al. 2003, Platt and Goetz 2004 HyMap hyperspectral digital data Schmidt et al. 2004 DAIS hyperspectral data Pal and Mather 2004 EO-1 Hyperion Apan et al. 2004…...

    [...]

  • ...In previous research, hyperspectral data have been successfully used for land-cover classification (Benediktsson et al. 1995, Hoffbeck and Landgrebe 1996, Platt and Goetz 2004, Thenkabail et al. 2004a, b) and vegetation mapping (McGwire et al. 2000, Schmidt et al. 2004)....

    [...]

  • ...…analysis (Myint 2001, Okin et al. 2001, Rashed et al. 2001, Asner and Heidebrecht 2002, Lobell et al. 2002, Neville et al. 2003, Landgrebe 2003, Platt and Goetz 2004) may be used for feature extraction, in order to reduce the data redundancy inherent in remotely sensed data or to extract…...

    [...]


Journal ArticleDOI
TL;DR: The models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10–100 m), and the advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms.
Abstract: The knowledge of impervious surfaces, especially the magnitude, location, geometry, spatial pattern of impervious surfaces and the perviousness–imperviousness ratio, is significant to a range of issues and themes in environmental science central to global environmental change and human–environment interactions. Impervious surface data is important for urban planning and environmental and resources management. Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention. In this paper, various digital remote sensing approaches to extract and estimate impervious surfaces will be examined. Discussions will focus on the mapping requirements of urban impervious surfaces. In particular, the impacts of spatial, geometric, spectral, and temporal resolutions on the estimation and mapping will be addressed, so will be the selection of an appropriate estimation method based on remotely sensed data characteristics. This literature review suggests that major approaches over the past decade include pixel-based (image classification, regression, etc.), sub-pixel based (linear spectral unmixing, imperviousness as the complement of vegetation fraction etc.), object-oriented algorithms, and artificial neural networks. Techniques, such as data/image fusion, expert systems, and contextual classification methods, have also been explored. The majority of research efforts have been made for mapping urban landscapes at various scales and on the spatial resolution requirements of such mapping. In contrast, there is less interest in spectral and geometric properties of impervious surfaces. More researches are also needed to better understand temporal resolution, change and evolution of impervious surfaces over time, and temporal requirements for urban mapping. It is suggested that the models, methods, and image analysis algorithms in urban remote sensing have been largely developed for the imagery of medium resolution (10–100 m). The advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms.

777 citations


Journal ArticleDOI
TL;DR: The experimental result shows that the proposed unsupervised band selection algorithms based on band similarity measurement can yield a better result in terms of information conservation and class separability than other widely used techniques.
Abstract: Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded, whereas computational burden is significantly relaxed. When the desired object information is known, this task can be achieved by finding the bands that contain the most information about these objects. When the desired object information is unknown, i.e., unsupervised band selection, the objective is to select the most distinctive and informative bands. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose unsupervised band selection algorithms based on band similarity measurement. The experimental result shows that our approach can yield a better result in terms of information conservation and class separability than other widely used techniques.

338 citations


Cites background from "A comparison of AVIRIS and Landsat ..."

  • ...For classes with similar but separable spectra, this is a reasonable assumption [19]....

    [...]


Journal ArticleDOI
TL;DR: A new supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples is proposed, which can complete the task much faster than traditional methods that test bands or band combinations.
Abstract: Band selection is often applied to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose a new supervised band-selection algorithm that uses the known class signatures only without examining the original bands or the need of class training samples. Thus, it can complete the task much faster than traditional methods that test bands or band combinations. The experimental result shows that our approach can generally yield better results than other popular supervised band-selection methods in the literature.

211 citations


Cites background from "A comparison of AVIRIS and Landsat ..."

  • ...For classes with similar but separable spectra, this is a reasonable assumption [13]....

    [...]


Journal ArticleDOI
TL;DR: It is found that the combination of segmentation into image objects, the nearest neighbor classifier, and integration of expert knowledge yields substantially improved classification accuracy for the scene compared to a traditional pixel-based method.
Abstract: Object-oriented image classification has tremendous potential to improve classification accuracies of land use and land cover (LULC), yet its benefits have only been minimally tested in peer-reviewed studies. We aim to quantify the benefits of an object-oriented method over a traditional pixel-based method for the mixed urban–suburban–agricultural landscape surrounding Gettysburg, Pennsylvania. To do so, we compared a traditional pixel-based classification using maximum likelihood to the object-oriented image classification paradigm embedded in eCognition Professional 4.0 software. This object-oriented paradigm has at least four components not typically used in pixel-based classification: (1) the segmentation procedure, (2) nearest neighbor classifier, (3) the integration of expert knowledge, and (4) feature space optimization. We evaluated each of these components individually to determine the source of any improvement in classification accuracy. We found that the combination of segmentation into image o...

199 citations


Cites background from "A comparison of AVIRIS and Landsat ..."

  • ...It is one of the most commonly used classifiers because of its simplicity and robustness (Platt and Goetz 2004)....

    [...]


References
More filters

BookDOI
17 Sep 1998
TL;DR: This chapter discusses Accuracy Assessment, which examines the impact of sample design on cost, statistical Validity, and measuring Variability in the context of data collection and analysis.
Abstract: Introduction Why Accuracy Assessment? Overview Historical Review Aerial Photography Digital Assessments Data Collection Considerations Classification Scheme Statistical Considerations Data Distribution Randomness Spatial Autocorrelation Sample Size Sampling Scheme Sample Unit Reference Data Collection Basic Collection Forms Basic Analysis Techniques Non-Site Specific Assessments Site Specific Assessments Area Estimation/Correction Practicals Impact of Sample Design on Cost Recommendations for Collecting Reference Data ASources of Variation in Reference Data Photo Interpretation vs. Ground Visitation Interpreter Variability Observations vs. Measurements What is Correct? Labeling Map vs. Labeling the Reference Data Qualitative vs. Quantitative Analysis Local vs. Regional vs. Global Assessments Advanced Topics Beyond the Error Matrix Modifying the Error Matrix Fuzzy Set Theory Measuring Variability Complex Data Sets Change Detection Multi-Layer Assessments California Hardwood Rangeland Monitoring Project Case Study Balancing Statistical Validity with Practical Reality Bibliography

4,394 citations


"A comparison of AVIRIS and Landsat ..." refers background in this paper

  • ...…well documented in domains such as mineralogical mapping (Chabrillat et al., 2002; P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G J u l y 2004 8 1 3 R.V. Platt was formerly with the Department of Geography, UCB 260, University of Colorado at Boulder, Boulder, CO 80309....

    [...]

  • ...While the advantages of high SNR are well documented in domains such as mineralogical mapping (Chabrillat et al., 2002; P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G J u l y 2004 8 1 3 R.V. Platt was formerly with the Department of Geography, UCB 260, University of Colorado at Boulder, Boulder, CO 80309....

    [...]

  • ...A pair-wise comparison of the kappa statistics (Rogan et al., 2002; Congalton and Green, 1998) for the two classifications shows that these results are significantly different from each other with a z-value of 78.32....

    [...]


OtherDOI
01 Jan 1976
TL;DR: The framework of a national land use and land cover classification system is presented for use with remote sensor data and uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources.
Abstract: The framework of a national land use and land cover classification system is presented for use with remote sensor data. The classification system has been developed to meet the needs of Federal and State agencies for an up-to-date overview of land use and land cover throughout the country on a basis that is uniform in categorization at the more generalized first and second levels and that will be receptive to data from satellite and aircraft remote sensors. The pro-posed system uses the features of existing widely used classification systems that are amenable to data derived from re-mote sensing sources. It is intentionally left open-ended so that Federal, regional, State, and local agencies can have flexibility in developing more detailed land use classifications at the third and fourth levels in order to meet their particular needs and at the same time remain compatible with each other and the national system. Revision of the land use classification system as presented in US Geological Survey Circular 671 was undertaken in order to incorporate the results of extensive testing and review of the categorization and definitions.

3,875 citations


"A comparison of AVIRIS and Landsat ..." refers background or methods in this paper

  • ...Less traditional sensors may provide additional information that can improve mapping accuracy....

    [...]

  • ...The classification system was a modification of Anderson Level II (Anderson et al., 1976) and used training samples from the following land use categories: residential, commercial/industrial, water, irrigated cropland, fallow, dry rangeland, grassland, and irrigated urban....

    [...]


Book
01 Jan 1986

2,822 citations


"A comparison of AVIRIS and Landsat ..." refers background in this paper

  • ...The classifier determines the probability that a pixel belongs to each class and then assigns the pixel to the class with the highest probability (Richards, 1999)....

    [...]


Journal ArticleDOI
Abstract: A transformation known as the maximum noise fraction (MNF) transformation, which always produces new components ordered by image quality, is presented. It can be shown that this transformation is equivalent to principal components transformations when the noise variance is the same in all bands and that it reduces to a multiple linear regression when noise is in one band only. Noise can be effectively removed from multispectral data by transforming to the MNF space, smoothing or rejecting the most noisy components, and then retransforming to the original space. In this way, more intense smoothing can be applied to the MNF components with high noise and low signal content than could be applied to each band of the original data. The MNF transformation requires knowledge of both the signal and noise covariance matrices. Except when the noise is in one band only, the noise covariance matrix needs to be estimated. One procedure for doing this is discussed and examples of cleaned images are presented. >

2,407 citations


"A comparison of AVIRIS and Landsat ..." refers background or methods in this paper

  • ...Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio (SNR)....

    [...]

  • ...After creating the synthetic Landsat image, a Maximum Noise Fraction (MNF) transform was performed on the AVIRIS cube and synthetic Landsat images to reduce processing time and noise, (Green et al., 1988....

    [...]


Journal ArticleDOI
Abstract: Imaging spectroscopy is of growing interest as a new approach to Earth remote sensing. The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was the first imaging sensor to measure the solar reflected spectrum from 400 nm to 2500 nm at 10 nm intervals. The calibration accuracy and signal-to-noise of AVIRIS remain unique. The AVIRIS system as well as the science research and applications have evolved significantly in recent years. The initial design and upgraded characteristics of the AVIRIS system are described in terms of the sensor, calibration, data system, and flight operation. This update on the characteristics of AVIRIS provides the context for the science research and applications that use AVIRIS data acquired in the past several years. Recent science research and applications are reviewed spanning investigations of atmospheric correction, ecology and vegetation, geology and soils, inland and coastal waters, the atmosphere, snow and ice hydrology, biomass burning, environmental hazards, satellite simulation and calibration, commercial applications, spectral algorithms, human infrastructure, as well as spectral modeling.

1,562 citations


"A comparison of AVIRIS and Landsat ..." refers background in this paper

  • ...Keywords AVIRIS, Landstat ETM+, Colorado, Urban Fringe, Signal to Noise Ratio Disciplines Environmental Indicators and Impact Assessment | Environmental Sciences This article is available at The Cupola: Scholarship at Gettysburg College: https://cupola.gettysburg.edu/esfac/8...

    [...]

  • ...Sensors with a large number of continuous spectral bands, such as AVIRIS, are called hyperspectral imagers (Green et al., 1998)....

    [...]


Frequently Asked Questions (1)
Q1. What are the contributions mentioned in the paper "A comparison of aviris and landsat for land use classification at the urban fringe" ?

In this study the authors tested whether AVIRIS data allowed for improved land use classification over synthetic Landsat ETM+ data for a location on the urban-rural fringe of Colorado. Furthermore, this advantage comes almost entirely from the large number of sensor spectral bands rather than the high Signal-to-Noise Ratio ( SNR ).