Comparison and integration of spaceborne optical and radar data for mapping in Sudan

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Comparison and integration of
spaceborne optical and radar data for
mapping in Sudan
Terry Idol
a
, Barry Haack
a
& Ron Mahabir
a
a
Department of Geography and Geoinformation Science, George
Mason University, Fairfax, VA, USA
Published online: 11 Mar 2015.
To cite this article: Terry Idol, Barry Haack & Ron Mahabir (2015) Comparison and integration of
spaceborne optical and radar data for mapping in Sudan, International Journal of Remote Sensing,
36:6, 1551-1569, DOI: 10.1080/01431161.2015.1015659
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Comparison and integration of spaceborne optical and radar data for
mapping in Sudan
Terry Idol, Barry Haack, and Ron Mahabir*
Department of Geography and Geoinformation Science, George Mason University, Fairfax,
VA, USA
(Received 16 October 2014; accepted 5 January 2015)
The purpose of this study was to determine how different procedures and data, such as
multiple wavelengths of radar imagery and radar texture measures, independently and
in combination with optical imagery influence land-cover/use classification accuracies
for a study site in Sudan. Radarsat-2 C-band and phased array L-band synthetic
aperture radar (PALSAR) L-band quad-polarized radar were registered with ASTER
(Advanced Spaceborne Thermal Emission and Reflection Radiometer) optical data.
Spectral signatures were obtained for multiple landscape features, classified using a
maximum-likelihood decision rule, and thematic accuracies were obtained using sepa-
rate validation data. There were surprising differences between the thematic accuracies
of the two radar data sets, with Radarsat-2 only having a 51% accuracy and PALSAR
73%. In contrast, the optical ASTER overall accuracy was 81%. Combining the
original radar and a variance texture measure increased the Radarsat-2 to 78% and
PALSAR to 80%, whereas the two original radar bands together had an accuracy of
87%. Sensor fusion of optical and radar obtained an accuracy of 93%. Based on these
results, the use of multiwavelength quad-polarized radar imagery combined or inte-
grated with optical imagery has great potential in improving the accuracy of land-
cover/use classifications. In tropical and high-latitude regions of the world, where
persistent cloud cover hinders the use of optical satellite systems, land management
programmes may find this research promising.
1. Introduction
Over the past several decades, spaceborne remote sensing has proved to be a highly useful
technology for the collection of reliable land-surface data sets. This has primarily been
accomplished by multispectral sensor systems, such as Landsat. The sensors in optical
systems, such as Landsat Thematic Mapper (TM), passively record the surface reflectance
of the sun’s energy in the visible and infrared spectral ranges. In contrast, synthetic
aperture radar (SAR) (radio detection and ranging) is an active sensor that emits and
receives wavelengths that are significantly longer than those detected by optical systems.
These longer wavelengths of radar can pass through atmospheric conditions, such as
clouds, that would otherwise obstruct the wavelengths of traditional spaceborne optical
and multispectral systems (Al-Tahir, Saeed, and Mahabir 2014; Henderson et al. 2002).
Another important benefit of radar is that it is not dependent on the sun’s energy, so it can
operate at night. The operational characteristics of radar have enormous data-collecting
potential for several geographic areas around the world, especially those often obscured
by persistent cloudy conditions, such as tropical and high-latitude regions.
*Corresponding author. Email: rmahabir@gmu.edu
International Journal of Remote Sensing, 2015
Vol. 36, No. 6, 1551–1569, http://dx.doi.org/10.1080/01431161.2015.1015659
© 2015 Taylor & Francis

A prior constraint on the use of radar is that most collected data from spaceborne
systems have been a single wavelength with a fixed polarization. Therefore, of the total
surface scattering information available, only one component is being measured. Any
additional surface scattering information contained within the returned radar signal is not
captured (Dell’Acqua, Gamba, and Lisini 2003; Töyrä, Pietroniro, and Martz 2001). More
recent systems, the Japanese phased array L-band synthetic aperture radar (PALSAR), the
Canadian Radarsat-2, and the German TerraSar-X and Sentinel systems, collect informa-
tion from multiple polarizations, which could potentially provide an immense amount of
land-cover/use information for areas that previously had little to no data available
(Sheoran and Haack 2013; Sawaya et al. 2010).
Polarization is important to remote-sensing scientists as each type of polarization
provides a different type of information. For example, VV polarization provides a good
contrast between small grain crops and broadleaf plants, whereas HH polarization pro-
vides greater information about soil conditions (Anys and He 1995). HV and VH provide
information about total biomass and are complementary to VV and HH polarization
(Campbell and Wynne 2012); McNairn and Brisco 2004). The contrast between vegetated
and cleared areas is best seen with HV polarization (Smith 2012).
Texture is a measure of the roughness or smoothness of an image. Texture measures
by themselves may not be able to achieve good classification accuracies, but recent
studies have shown that combining the original SAR image with texture measures
could lead to improved mapping accuracies (Sim et al. 2014; Amarsaikhan et al. 2007;
Lloyd et al. 2004; Herold, Haack, and Solomon 2004; Herold, Liu, and Clarke 2003;
Dekker 2003; Anderson 1998).
The intent of this study was to compare original radar and radar-derived texture
measures for land-cover/use classifications with the traditional optical or multispectral-
based classifications, and to evaluate sensor integration or fusion. One of the interesting
and unique components of the analysis was the opportunity to combine and classify radar
images from two different portions of the electromagnetic spectrum, each in quad-
polarization format.
2. Study data and site
The site selected for this analysis is Wad Madani, Sudan, in Northern Africa. Radar and
optical images over the study site were used to create land-cover/use classifications.
Radarsat-2 and PALSAR quad-polarization bands and derived texture measures were
combined and classified, and accuracy assessments were performed. Optical imagery
was collected by the ASTER (Advanced Spaceborne Thermal Emission and Reflection
Radiometer) instrument on board the Terra space shuttle mission. The ASTER imagery
had three spectral bands in the visible near-infrared region of the electromagnetic spec-
trum (bands 1, 2, and 3N (nadir looking)), each with a spatial resolution of 15 m.
Radarsat-2 was launched on 14 December 2007. It is the first commercial SAR
satellite to acquire C-band quad-polarization imagery. Radarsat-2 offers a wide range of
spatial resolutions (Canadian Space Agency 2008). A fine pixel resolution (8 m) quad-
polarization image was obtained for this study. The Advance Land Observation Satellite
(ALOS) was launched on 24 January 2006. On board the ALOS spaceborne platform is
the PALSAR sensor, which uses the L-band radar and is supported by the Japan
Aerospace Exploration Agency (JAXA). The spatial resolution from PALSAR was
12.5 m (JAXA 2006).
1552 T. Idol et al.

The Radarsat-2 image for Wad Madani was collected on 6 June 2009 during the rainy
season, which typically occurs from April to October. The ASTER image was captured on
4 March 2009, and the PALSAR data were collected on 12 May 2007. These differences
in acquisition dates do create some concerns, but since the primary goal is a relative
comparison of different processing methods and data combinations, those concerns should
be consistent for all classifications, thus allowing valid comparisons. The pixels of the
Radarsat-2, PALSAR, and ASTER images were all of different sizes. During the image-
to-image registration, the pixels were resampled to 10 m using the nearest neighbour
algorithm. In addition, the radiometric resolution of all data was consistently set at 8 bits
for the classification.
Figure 1 is a PALSAR composite image over the Wad Madani study area. The image
is approximately 22 km × 22 km. The analysis was based on a subset of the overlap of all
three data sets. Sudan’s major geographic feature is the Nile River and its tributaries,
which include the Blue Nile and the White Nile. The city of Wad Madani is nestled in a
bend on the west bank of the Blue Nile River. Wad Madani is located approximately
160 km southeast of Sudan’s capital city of Khartoum (Sawaya et al. 2010). In Figure 1,
the major landscape features including the Blue Nile, agriculture to the west, desert to the
northeast, and the city of Wad Madani on the west side of the Nile can be seen.
Figure 1. PALSAR 12 May 2007 image (HH, VV, and HV BGR) of Wad Madani. Centre image
coordinates 14.4° N, 33.5° E.
International Journal of Remote Sensing 1553

The following land-cover/use features were classified using the Anderson et al. (1976)
classification system: dense urban, fallow agriculture and/or bare ground, sparse natural
trees, water, and irrigated agriculture. One of the issues with this study area was the
variety of crops at different stages in their growth cycle. This required careful selection of
calibration and validation sites. The fallow agricultural fields and the extensive areas of
bare ground particularly to the east of the Blue Nile were not different in either the optical
or radar images and thus combined. The urban class is very concentrated in Wad Madani
with smaller but dense villages, and there are limited areas of sparse forest primarily near
the river.
The width of the Blue Nile River is extremely narrow, fluctuating between 280 and
460 m. This narrow waterbody size could cause issues when using larger pixels for
classifications or with some window-derived values. Moreover, the classes used in this
study are generalized and limited in number. However, for a comparison of methods and
data, they were considered sufficient. At a future research stage based on results from this
study, more detailed classes might be incorporated.
3. Methodology
The land-cover/use classification consisted of three components. First, the calibration sites
for the classification were identified. Second, the classifications were generated. Finally,
the thematic accuracy results of the classifications were determined using separate valida-
tion sites. Calibration and validation areas of interest (AOIs) were collected via AOI
polygons. These polygons were determined using knowledge of the area, visual inspection
of the various remote-sensing data, and use of finer spatial resolution imagery from
Google Earth. The calibration AOIs identified the spectral characteristics of each of the
classification categories. The validation AOIs were employed to determine the thematic
accuracy of the land-cover/use classifications. For both calibration and validation, two to
four AOIs were selected for each class. There is extensive remote-sensing literature on the
various issues relative to accuracy assessments including sample type (pixel or polygon),
sample size, sample selection, and statistical evaluations (Foody 2002; Smits, Dellepiane,
and Schowengerdt 1999). Generally, pixels selected randomly by strata or class are
preferred. The primary research focus in this study was on the relative thematic accuracies
of individual classes and overall for various sensor types, derived values, and combina-
tions of data and not on the accuracy of the map products. This study employed validation
AOIs that may not provide the best accuracies but in the opinion of the authors are
appropriate for relative evaluations of different data types and combinations of data, the
focus of this study.
The maximum-likelihood decision rule was applied for the classifications. Similar to
the literature on different approaches for validation, there are different methods of
signature extraction, signature evaluation, and decision rules for classification.
Maximum likelihood is very standard and, for a comparison of data and data integrations,
will provide useful comparisons. Moreover, because maximum likelihood assumes that
classes are multivariate normal in distribution (Richards and Jia 2005), special care was
taken to ensure that pure end members of classes were selected during the extraction of
calibration and validation sites. Both sets of AOIs were kept separate during the classi-
fication process and were therefore exclusive in use throughout the process. Other
decision rules such as support vector machines may provide higher accuracies but are
not likely to change the relative results. The following section presents the results of the
1554 T. Idol et al.

various classifications beginning with the independent ASTER and radar images and then
progressing to various value added, texture evaluations, and data combinations.
4. Results
4.1. ASTER classification
The analysis of optical imagery to perform land-cover/use classification is not the goal of
this research. However, the results of classifications obtained by using the optical imagery
can be a baseline against which the results of radar can be compared. Table 1 lists the
results for the ASTER imagery analysis. The Wad Madani optical land-cover/use classi-
fication results are good in most classes, ranging from 55% to 99% in the producer’s
accuracy and from 65% to 100% in the user’s accuracy. The overall accuracy is 81% for
the ASTER imagery.
The greatest classification confusion for the Wad Madani ASTER imagery was with
sparse trees. Significant geographic areas that comprised sparse trees were misclassified as
both agriculture and urban areas. The sparse trees classification had errors of omission and
commission (producer’s and user’s accuracies) with both of these other classes. The
confusion with agriculture is understandable as they both generally have green vegetation.
The confusion with urban may be caused by some trees in the urban landscape and also
the sparse trees containing bare soil similar in spectral response to rooftops. The ASTER
image was taken during the dry season, so the plants were not as well developed as they
would be during the rainy season. This condition might also explain the confusion
between sparse trees and agriculture. There were some user misclassifications with the
urban class with both bare soil and sparse trees. This could be anticipated, as the urban
area contains some open areas and some plants. In addition, the urban structures often use
indigenous materials, such as clay and bricks, which are spectrally similar to bare soil,
part of the sparse forest landscape. Nevertheless, the classification results for Wad Madani
from the ASTER imagery are very reasonable.
4.2. Radar analysis
One of the ongoing issues with radar is the necessity or appropriateness of removing, or at
least reducing, the amount of speckle (Maghsoudi, Collins, and Leckie 2012;
Bouchemakh et al. 2008; Lu et al. 1996). The amount of speckle varies between radar
Table 1. Error matrix for ASTER, Wad Madani.
Reference
Water
Bare
soil
Sparse
trees Agriculture Urban
User’s
accuracy (%)
Classified Water 16,531 0 0 0 0 100.0
Bare soil 0 10,883 87 0 1623 86.4
Sparse trees 101 0 9861 3926 1379 64.6
Agriculture 2 0 4698 14,575 0 75.6
Urban 0 1247 3368 296 17,048 77.6
Producer’s
accuracy (%)
99.4 89.7 54.7 77.5 85.0 80.5

data sets, as a function of the level of vendor preprocessing and also if the radar
acquisition was single- or multilook. Single-look radar will typically have more speckle
than multilook radar since multilook divides the SAR returns in the pass, creating
different images that are then averaged into a single image.
In this study, a comparison was made between the spectral signatures and thematic
classifications of original radar and despeckled radar at both 3 × 3 and 5 × 5 windows
using the Lee–Sigma algorithm. The statistical values of the spectral signatures for the
different land-cover/use classes for the despeckled PALSAR image are listed in Table 2.
Only values for the HH and HV polarizations are included since the results of HH and
VV, and HV and VH were very similar. These statistical values can provide information
on how well the different classes are statistically separated, which in turn can provide
insight into how well classifications might be. As would be anticipated, the large window
sizes have lower standard deviations. This was also noticed for both HH and HV
polarizations for the Radarsat-2 despeckled image (results not shown) when moving
from a 3 × 3 to a 5 × 5 window size.
The high mean digital number (DN) value for the water class for the PALSAR image
is unusual. Close examination of the imagery does not explain the high water values.
Sparse natural trees in the image display a low, although mixed as indicated by the high
standard deviation, radar return. The forest areas provide higher mean DN values than
bare soil, which suggests that there will be little confusion between the two classes. It is
interesting that the water and trees are very similar in HH but different in HV. No such
unexpected results were found for the Radarsat-2 values.
Table 2. Spectral signatures of Wad Madani despeckled PALSAR image.
PALSAR imagery 3 × 3 Window 5 × 5 Window
Land-cover/use classes
Example AOI statistics
digital numbers (DNs) HH HV HH HV
Water X 139.8 93.7 139.7 93.3
σ 23.9 31.0 20.5 29.2
Minimum value 77.0 40.0 90.0 42.0
Maximum value 255.0 180.0 238.0 154.0
Bare soil X 67.2 79.6 67.0 79.5
σ 23.8 18.4 22.0 16.3
Minimum value 19.0 31.0 23.0 40.0
Maximum value 210.0 165.0 189.0 150.0
Sparse natural trees X 160.5 192.0 160.2 197.8
σ 46.3 56.3 43.8 54.4
Min. value 59.0 72.0 63.0 75.0
Max. value 255.0 255.0 255.0 255.0
Agriculture X 157.2 102 157.0 101.9
σ 33.0 22.3 30.0 20.1
Minimum value 57.0 26.0 62.0 30.0
Maximum value 255.0 193.0 245.0 188.0
Urban X 241.6 254.7 241.5 254.6
σ 22.4 2.8.0 19.8 2.2
Minimum value 93.0 203.0 93.0 219.0
Maximum value 255.0 255.0 255.0 255.0
Note: Here X is the mean and σ is the standard deviation.
1556 T. Idol et al.

Analysis of the PALSAR imageries’ spectral signature values suggests that it may be
difficult to differentiate between sparse natural trees and agriculture land cover. In the
PALSAR images, both classes have very similar spectral values in the HH bands.
However, the HV band has much greater differences. The urban areas have a high
mean spectral signature value in both bands for the PALSAR image. This suggests that
the urban class will have little likelihood of confusion with other classes.
Table 3 shows the confusion matrix with classification results for the Wad Madani
Radarsat-2 and PALSAR imagery using a despeckled 5 × 5 window. As would be
expected, particularly given the use of polygons for accuracy assessment because the
despeckling is essentially a smoothing filter, the larger window size despeckled data had
higher overall thematic accuracies. These differences, however, were relatively small. The
Radarsat-2 original accuracy was 51%, which increased to 58% with the 5 × 5 filter,
whereas the PALSAR accuracy increased from 73% to 79%. Despeckled radar at a 5 × 5
window was used in this study.
As shown in Table 3, there was a great deal of confusion between the water and bare
soil classes within the Radarsat-2 images. In the Radarsat-2 image, the producer’s
accuracy for water was very good, 93% with a low user’s accuracy of 60%. Given the
small width of the Blue Nile, the larger window size may have influenced these results.
This confusion was also evident in the poor producer’s accuracy for bare soil, which
achieved extremely poor results at 20%, likely because the water and bare soil both act as
specular reflectors with similar low backscatter. However, the PALSAR bare soil produ-
cer’s accuracy result was very high at 98%. Also, the sparse trees producer’s accuracy was
low for both Radarsat-2 and PALSAR images. In the Radarsat-2 images, sparse trees were
confused with bare soil, agriculture and urban. However for the PALSAR imagery, the
sparse tree classification experienced a high rate of confusion with the urban class.
Table 3. Error matrices for Wad Madani classification using despeckled 5 × 5 window.
Reference
Water
Bare
soil
Sparse
User’s
accuracy (%)
Wad Madani–Radarsat-2
Bare soil 1145 2409 1266 2621 632 29.8
Sparse trees 1 18 9434 3100 4179 56.4
Agriculture 2 145 5390 11,777 4636 53.7
Urban 0 0 1721 727 10,478 81.1
Producer’s
accuracy (%)
93.1 19.9 52.4 62.7 52.3 57.9
Wad Madani–PALSAR
Bare soil 259 11,910 63 5150 0 84.4
Sparse trees 2 16 12,569 428 1594 84.8
Agriculture 1322 166 1174 9592 0 73.2
Urban 0 0 3932 0 18,456 82.4
Producer’s
accuracy (%)
90.5 98.2 69.8 51.0 92.0 78.9

As was expected, the Radarsat-2 agriculture classification producer’s accuracy was
low at 63%, with a great deal of confusion between the agriculture, bare soil, and sparse
trees. The producer’s accuracy for the agriculture class in the PALSAR imagery was also
low at 51%. Most of the confusion within the agriculture classification in the PALSAR
image was with the water and the bare soil classes. The spectral signature mean values for
bare soil and agriculture were very different. Confusion between agriculture and sparse
trees was expected, but was minimal. The spectral signature values between agriculture
and water were similar, so the confusion between the two classes was not unexpected.
Considering that the urban bands in the PALSAR image were separated from all the
other classes, the high 92% producer’s accuracy results were expected. However, it was
anticipated that the Radarsat-2 images would yield better producer’s accuracy than was
actually achieved. This expectation was based on the urban spectral signatures for the HH
and HV bands that were well separated from the other classes. The Radarsat-2 image’s
urban producer’s accuracy was only 52%.
4.3. Texture analysis
Remote-sensing data are a compilation of both brightness value for each pixel (spectral)
and arrangement of the pixels (spatial). This spatial information can be extracted as
textural information from the pixels (Cervone and Haack 2012; Champion et al. 2008;
Chen, Stow, and Gong 2004; Kurosu et al. 1999). Traditional digital image classification
methodologies are based purely on the use of the spectral characteristics of the data, thus
ignoring any spatial information in the data collected (Maillard 2003). Areas such as
residential or urban are more easily distinguished by their spatial characteristics
(Nyoungui, Tonye, and Akono 2002; Solberg and Anil 1997). Ignoring the full comple-
ment of data collected creates challenges for the accurate classification of land-cover/use
classes. The analysis of texture was therefore an important component of this study.
The use of radar texture measures in land-cover/use classification has generated varied
results. In some literature, texture layers have yielded better classification results than the
original radar images (Haack, Solomon, and Herold 2002; Kiema 2002). In other litera-
ture, the classification results from a texture measure layer were not as good as the
original radar image. Often combining original radar and derived texture assists in
improved classifications, at least for some classes (Herold, Haack, and Solomon 2004).
Two types of analysis were performed using texture layers. First, variance texture
measures were extracted for four different window sizes for each band of the original, not
despeckled, Radarsat-2 and PALSAR data. The use of variance texture was guided by
previous work, which determined this measure to be suitable for mapping land cover/use
from radar imagery (Herold, Haack, and Solomon 2004; Haack and Bechdol 2000). Also,
as suggested by Ulaby et al. (1986), several texture measures extracted from the grey-level
co-occurence matrix correlated. This statement is exemplified in the work of Marceau
et al. (1990), finding only 7% and 3% of variance explained by texture measures and
quantization level, respectively, the remaining 90% of which was explained by window
sizes. Because this study focuses on the parameter of most importance, scale as suggested
by Marceau et al. (1990), the extraction of all texture features was limited to the use of the
variance measure. The window sizes evaluated were 5 × 5, 9 × 9, 13 × 13, and 17 × 17.
Larger windows have given higher results in earlier studies (Tadesse and Falconer 2014),
but research has also shown that any window size larger than 13 × 13 often gives
diminishing returns (Villiger 2008). Second, each of the despeckled radar images (5 × 5
window) was combined (layer stacked) with the best of the texture measure that was
1558 T. Idol et al.

generated from that specific image. The best texture measure was determined by the
window size with the highest overall classification accuracy. The combined image was
classified and the results were analysed.
Error matrices for the best texture measures created using the original Wad Madani
Radarsat-2 and PALSAR images are shown in Tables 4 and 5. Each of the four texture
windows for the Radarsat-2 image produced a land-cover/use classification accuracy that
was superior to the classification for the original image, that is, overall classification
results for each derived texture measure exceeding 58%. The overall accuracies increased
with window size from 60% for the 5 × 5 window to 78% for the 17 × 17 window, and the
results of which are detailed in Table 4.
Conversely, none of the texture measures generated with the PALSAR image pro-
duced land-cover/use classification results that were as good as the classification of the
original despeckled image, with all overall low accuracy values less than 55% (Table 5).
The best texture overall (55%) was the largest window and the percentage decreased to
41% for the 5 × 5 window. These results by window size were not surprising as texture
acts as a spatial filter, and in using AOIs for validation, filtering would generally increase
Table 4. Wad Madani error matrices of Radarsat-2 variance texture measures.
Texture measure 17 × 17
Reference
Water
Bare
soil
Sparse
User’s
accuracy (%)
Bare soil 1072 4493 29 2029 0 58.9
Sparse trees 31 0 17,075 5749 1497 70.1
Agriculture 50 42 523 10,989 32 94.4
Urban 0 0 387 30 18,521 97.8
Producer’s
accuracy (%)
93.1 37.0 94.8 58.5 92.4 77.7
Table 5. Wad Madani error matrices of PALSAR variance texture measures.
Texture measure 17 × 17
Reference
Water
Bare
soil
Sparse
User’s
accuracy (%)
Classified Water 5883 131 2469 3086 1731 43.1
Bare soil 2602 11,239 999 9447 975 54.9
Sparse trees 641 42 8670 481 785 71.4
Agriculture 6737 228 3675 5254 994 27.8
Urban 756 490 2201 529 15,565 78.9
Producer’s
accuracy (%)
35.4 92.7 48.1 28.0 77.6 55.4

thematic accuracies. Note, however, that the original radar classifications were with
despeckled data, and the texture analyses were not despeckled. Classified images of
Wad Madani using the best texture measure are shown in Figure 2.
Closer examination of the Radarsat-2 data shows that as the texture measure window
size increased, there was very good improvement in the producer’s accuracy in the urban
and sparse tree classes. However, the water class decreased very slightly in producer’s
accuracy as the window size increased, most likely as a function of the larger window size
including some non-water areas. The agriculture class showed a small increase in produ-
cer’s accuracy with an increase in window size. Compared to the results of the Radarsat-2
texture measures, the PALSAR despeckled 5 × 5 image overall classification result was
79%. The classification result for the texture measure generated from the PALSAR
original image with a window size of 17 × 17 is 55%, a decrease of 24%. These results
are surprising, but the high original PALSAR data accuracy results allow few opportu-
nities for improvement.
It is interesting to note that where Radarsat-2 had difficultly classifying bare soil
properly with a producer’s accuracy value of 37%, it did well classifying water with an
accuracy of 93%. PALSAR classification producer’s accuracies showed the opposite
trend. PALSAR did very well classifying the bare soil with the highest producer’s
classification accuracy of 93% and a corresponding water accuracy value of 35%. This
may well correspond to the way the Radarsat-2’s C-band wavelength interacts with the
water and bare soil versus the PALSAR L-band wavelength.
In most cases, the larger texture measures window sizes achieved better results than
the smaller window sizes. Additionally, it was interesting to note that the best classifica-
tion accuracy improvements were seen in the urban class. In a few cases, increases in
areas classified as urban caused a decrease in overall classification results.
The Radarsat-2 texture measures gave better classification results than the despeckled
original images. The PALSAR texture measures provided either very slight improvements
or much worse classification results from a land-cover/use class perspective. It appears
that the L-band does not perform as well as the C-band when classifying land cover/use
using a texture measure.
Figure 2. Classification occurring over Wad Madani. (a) Classification completed using Radarsat-
2 January. Texture measure 17 × 17. (b) Classification completed using PALSAR. Texture measure
17 × 17 (water, blue; agriculture, light green; bare soil, grey; sparse trees, dark green; urban, red).
Approximate scene width 15 km.
1560 T. Idol et al.

Both 5 × 5 despeckled original radar images were combined (layer stacked) with the
best of the texture measures that were generated for that specific image for analysis. For
example, the Radarsat-2 5 × 5 despeckled image was combined with the best texture
measure that was created from that image. The combined image was then classified. The
results were analysed and compared with the classification results of the original des-
peckled image alone. Table 6 provides the results of these combinations for both radar
sensors.
The land-cover/use classification of both Wad Madani original despeckled images
combined with the best texture measure images showed substantial increases when
compared to the overall accuracy of the despeckled-only radar classification. The land-
cover/use classification of the Radarsat-2 despeckled image was combined with the best
texture measures image, the 17 × 17 window. This combination resulted in an overall
accuracy of 78%, an improvement, when compared to the despeckled-only radar image
classification of 58%, or an increase of 20%. The overall classification accuracy of the
PALSAR original despeckled image combined with the best texture measures image, the
17 × 17 window, also improved slightly when compared to the classification of the
PALSAR original despeckled image, from 79% to 80%.
Analysis of individual classes showed that the water classification values were already
high. Adding texture did little to raise the water classification accuracy values. The
addition of texture measures to the original imagery greatly enhanced the urban and
sparse tree classes producer’s and user’s accuracy. For example, for the Radarsat-2
image, the urban class producer’s accuracy was improved by 40%. The texture measures
in these classes, when added to the original image, were able to greatly enhance the
classification results. The results in the PALSAR image were lower, as the urban
Table 6. Error matrices of Wad Madani original despeckled imagery combined with the best
texture measure.
Reference
Water
Bare
soil
Sparse
User’s
accuracy (%)
Wad Madani – Radarsat-2 original and texture 17 × 17
Bare soil 1291 4639 37 1599 0 61.3
Sparse trees 26 5 16,977 5701 1480 70.2
Agriculture 21 21 569 11,476 39 94.6
Urban 0 0 431 21 18,531 97.6
Producer’s
accuracy (%)
92.0 38.2 94.2 61.1 92.4 78.2
Wad Madani – PALSAR original and texture 17 × 17
Bare soil 138 11,743 0 5994 0 65.7
Sparse trees 128 49 14,010 533 1547 86.1
Agriculture 1863 221 1307 10,029 26 74.6
Urban 0 0 2674 0 18,450 87.3
Producer’s
accuracy (%)
87.2 96.8 77.8 53.4 92.1 80.3

producer’s accuracy had the same value when compared to the original image and the
sparse trees user’s value only increased by 8%. The addition of texture helped differentiate
the bare soil class from water and sparse trees in the Radarsat-2 images. In the original
Radarsat-2 image alone, the producer’s accuracy of the bare soil was 19%. When texture
was added, this increased to 38%, which is still low.
4.4. Combining multiple-wavelength radar images
This section explores the use of the relatively new opportunity of combining and
classifying radar images from two different portions of the electromagnetic spectrum.
The PALSAR sensor collects data in the L-band, whereas Radarsat-2 acquires data in the
C-band. As both satellites collect data in different wavelengths, it is anticipated that
combining the two images would increase the information and thus improve the classi-
fication results. All of the images used in this analysis were despeckled with a 5 × 5
window size.
Combining radar images from two different portions of the electromagnetic spectrum
provided improvements when compared to a single image (Table 7). The best accuracy
achieved with a single Wad Madani radar image was 78%, when using the PALSAR
image. When the Wad Madani Radarsat-2 image was layer stacked with the PALSAR
image and classified, the overall accuracy result increased to 87%, an improvement of 9%.
Most confusion between individual classes in the combined Radarsat-2 and PALSAR
images occurred between agriculture and bare soil. This was not expected. The producer’s
accuracy for the sparse trees class did improve slightly in the Wad Madani Radarsat-2 and
PALSAR combination. This improvement would be expected, as more foliage during the
rainy season can improve the texture and radar returns, helping differentiate sparse trees
from the other classes. Overall, however, every class had very good results with the
classification.
4.5. Combining optical and radar images
This final analysis examines whether combining the radar and texture measures generated
from radar with the ASTER multispectral image can improve overall classification results.
All three ASTER bands were layer stacked and used in the analysis. The use of multiple
Table 7. Error matrix of Wad Madani original Radarsat-2 and PALSAR despeckled combined
imagery.
Reference
Water
Bare
soil
Sparse
User’s
accuracy (%)
Bare soil 171 11,974 1 2914 0 79.5
Sparse trees 20 13 13,810 1382 1493 82.6
Agriculture 435 51 638 14,119 0 92.6
Urban 0 0 3557 0 18,557 83.9
Producer’s
accuracy (%)
96.2 98.7 76.7 75.1 92.6 87.0
1562 T. Idol et al.

radar wavelengths in combination with ASTER imagery in land-cover/use classification is
relatively unique as prior work has used only one radar wavelength (Amarsaikhan
et al. 2012; Santos and Messina 2008).
For the Wad Madani area, the best classification results for the original despeckled
images were achieved when using the PALSAR scene. The PALSAR image was com-
bined with the ASTER optical image for classification. Next, the Wad Madani Radarsat-2
texture measure with a window size of 17 × 17 resulted in the best overall accuracy results
for the single-texture measures. This layer was then combined with the ASTER image,
which yielded another error matrix. Finally, the best texture measure, which was the
Radarsat-2 texture measure with a window size of 17 × 17, and the best of the original
despeckled radar, which was the PALSAR image, were layer stacked with the ASTER
image. Table 8 provides the confusion matrix for the best of the above-mentioned layer
combinations, which is the ASTER and PALSAR combination at 93%. The other sensor
fusion results had similar overall accuracies and minor class-by-class variations. The
ASTER and Radarsat texture overall accuracy was 92% and the ASTER, PALSAR, and
Radarsat texture was 92%.
When the PALSAR image was added to the ASTER optical image, the overall
accuracy increased to 93% relative to the 80% of the ASTER electro-optical image
alone. The largest increase in producer’s accuracy occurred with the sparse trees class.
This class performed very poorly in the ASTER-only classification, with a producer’s
accuracy of 55%. When the ASTER, PALSAR, and Radarsat-2 texture measure images
were combined, the sparse trees class producer’s accuracy rose to a very high 98%, an
increase of 43%. In general, when the radar imagery was added to the ASTER image, the
overall accuracy improved. In the case of Wad Madani, the overall accuracy increased
substantially by 11–13%.
5. Discussion and conclusions
Use of radar in land-cover/use applications continues to increase, driven in part by the
widespread online data availability. With the increase in the quantity of available radar
imagery, it is important to understand both strengths and weaknesses of using radar for
land-cover/use classifications. Table 9 lists the overall thematic accuracies for the various
sensors, derived texture values, and data combinations for this study. As noted previously,
there are some individual class variations in accuracies that also are important and overall
Table 8. Wad Madani optical, SAR, and texture combinations error matrices.
Reference
Water
Bare
soil
Sparse
User’s
accuracy (%)
Bare soil 0 12,126 74 37 0 99.1
Sparse trees 330 4 16,131 2128 636 83.9
Agriculture 628 0 971 16,632 0 91.2
Urban 0 0 838 0 19,414 95.9
Producer’s
accuracy (%)
94.2 100.0 89.5 88.5 96.8 93.4

accuracy should not be the sole evaluation measure. The results for the classifications
using the ASTER imagery alone were excellent (81%), thereby reinforcing the use of
optical imagery as a valued resource for land-cover/use classification. If optical imagery
could be collected regardless of weather conditions and at either day or night, an argument
could be made that radar data would have a much more limited use. However, in many
parts of the world, such as the tropics and high latitudes, it is difficult to collect optical
imagery. Therefore, as more radar imagery becomes available, it will be used more
frequently to examine those parts of the world where optical imagery is unavailable.
There are of course other potential applications of radar than land cover/use, including
biomass estimations (Kurvonen, Pulliainen, and Hallikainen 1999; Luckman et al. 1997)
and deformation via interferometric approaches (Rosen et al. 1996; Massonnet, Briole,
and Arnaud 1995).
Even when optical imagery is available, radar imagery can help improve the classi-
fication results. Such efforts have not been restricted to land-cover/use applications,
including its use in geology (Ricchetti 2001; Yesou et al. 1993), floods (Wang,
Koopmans, and Pohl 1995), and in the identification of coal fire-affected areas (Prakash
et al. 2001). In general, as reported in this study, when the radar imagery was added to the
ASTER optical image, the overall accuracy improved, and for the Wad Madani area, the
overall accuracy increased substantially (93%). Similar increased accuracy, compared to
individual optical or radar, was found by Laurin et al. (2013) investigating land cover in
West Africa. Using images collected from the Landsat TM and the Advanced Visible and
Near-Infrared Radiometer type-two optical sensors, Laurin et al. (2013) reported accura-
cies of 95.6% and 97.5% for both sensors, respectively. Likewise, Forkuor et al. (2014)
reported radar contributions in the range of 10–15% when radar was integrated with
optical imagery for crop mapping in Northwestern Benin, West Africa. These results are
not surprising given the complementary nature of both sets of data. In the case of optical
imagery, chemical, physical, and biological characteristics of target objects are provided.
Radar data are associated with the shape, texture, structure, and dielectric properties
(Pereira et al. 2013). However, at least in the aforementioned studies, the use of dual-
pole radar was investigated compared to quad-polarized data used in the present study.
Nonetheless, both the present study and others show the increase value added in the
combined use of optical and radar data for land-cover/use applications.
In the radar analyses using a texture measure, in most cases, the larger window sizes
achieved better results than the smaller window sizes. The 17 × 17 window size provided
the best results. Additionally, it was interesting to note that the best classification accuracy
Table 9. Summary by data type of overall accuracies.
Data combination Overall accuracy (%)
ASTER 80.5
Radarsat (despeckled) 57.9
PALSAR (despeckled) 78.9
Radarsat variance texture 77.7
PALSAR variance texture 55.4
Radarsat and texture 78.2
PALSAR and texture 80.3
Radarsat and PALSAR 87.0
ASTER and PALSAR 93.4
1564 T. Idol et al.

improvements for the original radar imagery were seen in the urban class. The Radarsat-2
texture measures resulted in better classifications than the despeckled original images. It
appears that the PALSAR L-band does not perform as well as the Radarsat-2 C-band
when generating classifications while using a variance texture measure. Conversely, Lu
et al. (2011) found the opposite relationship for the results of both sensors. However, these
results would have been influenced by the difference in fusion method used and subsets of
land-cover/use classes chosen for examination in the particular study. These differences
highlight the increasing need for the increased replication of scientific approaches over
different geographic areas for more objective comparisons. Moreover, as further reported
in the present study, the classification results of the combined original radar and texture
images showed substantial increase when compared to the overall accuracy of the
despeckled-only radar image classification results.
This study also explored the relatively new opportunity of combining and classifying
radar images from two different portions of the electromagnetic spectrum. Previous
studies such as Liao, Huang, and Guo (2004) have examined the fusion of multiple
C-band images, providing relatively good results. With the combination of different
wavelengths, the expectation is that higher land-cover/use classification accuracies will
result. This continues to be an area of increasing interest to the remote-sensing commu-
nity. In line with other similar studies (Evans et al. 2010; Amarsaikhan et al. 2007), the
combination of radar images consistently provided improvements over the use of a single
radar image. These findings therefore support the use of radar multiwavelength imagery
having considerable potential for land-cover/use classification (80% for the two des-
peckled radar wavelengths).
The final portion of this research was to determine whether or not the combination of
radar imagery and texture measures generated from radar imagery with the ASTER
images could improve overall classification results. When the radar imagery was added
to the ASTER image, in general, the overall accuracy improved. In the case of the Wad
Madani site, the overall accuracy increased considerably, an increase of 11–13%.
Based on the results of this research, radar land-cover/use classification accuracy can
in some situations almost equal or perhaps surpass that of optical imagery. This study
shows that there is great promise that areas of the world that were largely unseen due to
cloud cover can now be exposed. There will be several new areas of research, given the
new radar sensors that are now being deployed. The Sentinel satellite missions from the
European Space Agency, starting with the launch of Sentinel-1 on 3 April 2014, present a
good example of the trend towards the increased provision of free and global coverage
radar imagery. Sentinel-1 is equipped with a single polarization (VV or HH) for the Wave
Mode and selectable dual polarization (VV + VH or HH + HV) for all other modes.
Furthermore, with spatial resolutions of 5 × 5 m, 5 × 20 m, 5 × 20 m, and 25 × 100 m for
strip map, interferometric-wide, wave, and extra-wide swath viewing modes, it is expected
that this data source will be widely used for land-cover/use mapping.
Overall, the results of this study support the increased use and greater research of radar
for land-cover/use mapping. In the future, several other areas are to be investigated,
extending the present research. Of particular interest is the investigation of multitemporal
radar. Several studies including those of Chust, Ducrot, and Pretus (2004), Shao et al.
(2001), Le Hegarat-Mascle et al. (2000), and Pierce et al. (1998) have examined this area,
showing substantial benefits for the discrimination of vegetation, especially those having
distinct phonological cycles. Other areas to be investigated include use of more detailed
land-cover/use classifications, comparison of other texture measures such as those pro-
posed by Haralick, Shanmugam, and Dinstein (1973), the use of other data fusion

methods such as principal component analysis, and investigation of other classification
algorithms, such as neural network, decision tree, support vector machine, object-based
algorithms, sub-pixel-based algorithms, and contextual algorithms. These are not new
areas of research as reported in the works of Pereira et al. (2013), Li et al. (2012), Qi et al.
(2010), and Gao and Ban (2009). However, in order for the field of radar remote sensing
as it applies to land-cover/use mapping to mature fully, increasingly, more work needs to
be carried out in these areas so that both meaningful discussion and validation of research
findings can be obtained.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
The authors would like to thank the following organizations for providing and/or funding the imagery
used and for supporting this research. Radarsat-2 images were provided by the Canadian Space
Agency under project3126 of the Science and Operational Application Research for Radarsat-2
program. The Alaska Space Facility, under sponsorship from NASA, provided the PALSAR imagery.
The NASA Land Processes Distributed Active Archive Center at the USGS/Earth Resources
Observation and Science (EROS) Center provided the ASTER imagery. Finally, additional support
was provided through grants received from the Department of Geography and Geoinformation Science
at George Mason University.
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