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Geocarto International
ISSN: 1010-6049 (Print) 1752-0762 (Online) Journal homepage: http://www.tandfonline.com/loi/tgei20
An evaluation of Radarsat-2 individual and
combined image dates for land use/ cover
mapping
Terry Idol, Barry Haack & Ron Mahabir
To cite this article: Terry Idol, Barry Haack & Ron Mahabir (2015): An evaluation of Radarsat-2
individual and combined image dates for land use/ cover mapping, Geocarto International,
DOI: 10.1080/10106049.2015.1120351
To link to this article: http://dx.doi.org/10.1080/10106049.2015.1120351
Accepted author version posted online: 18
Nov 2015.
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Publisher: Taylor & Francis
Journal: Geocarto International
DOI: http://dx.doi.org/10.1080/10106049.2015.1120351
An evaluation of Radarsat-2 individual and combined image dates for land
use/ cover mapping
TerryIdol1
, BarryHaack2
, RonMahabir3
*
1
George Mason University, Department of Geography and Geoinformation Science,
Fairfax, Virginia, USA; E-Mail: tidol@gmu.edu; +1-703-993-1210
2
George Mason University, Department of Geography and Geoinformation Science,
Fairfax, Virginia, USA; E-Mail: bhaack@gmu.edu; +1-703-993-1215
3
George Mason University, Department of Geography and Geoinformation Science,
Fairfax, Virginia, USA; E-Mail: rmahabir@gmu.edu; +1-703-993-1210
* Correspondence author; E-Mail: rmahabir@gmu.edu
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An evaluation of Radarsat-2 individual and combined image dates for land
use/ cover mapping
Various land use/cover types exhibit seasonal characteristics which can be
captured in remotely sensed imagery. This study examined how different
seasons of Radarsat-2 data influence land use/cover classification accuracies
for two study sites. Two dates of Radarsat-2 C-band quad-polarized images
were obtained for Washington, D.C., USA and Wad Madani, Sudan. Spectral
signatures were extracted and used with a maximum likelihood decision rule
for classification and thematic accuracies were then determined. Both
despeckled radar and derived texture measures were examined. Thematic
accuracies for the two despeckled image dates were similar with a difference of
3% for Washington and 6% for Sudan. Merging the despeckled images for
both seasons increased overall accuracy by 2% for Washington and 9% for
Sudan. Further combining the original radar for both seasons with derived
texture measures increased overall accuracies by 9% for Washington and 16%
for Sudan for final overall accuracy values of 73% and 82%.
Keywords: Radarsat-2; quad-polarization; multidate; multitemporal; texture;
Sudan; Washington DC
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1. Introduction
The ability to obtain accurate and timely information on multiple surface conditions has
frequently been demonstrated by both airborne and spaceborne remote sensing methods.
That information can be for single features such as mapping deforestation as well as for
complex multiple land use/cover class mapping. Most of these activities have been
accomplished, especially via spaceborne platforms, with multispectral sensors (Idol et al.
2015a). Currently, there are over 60 of these spaceborne civil sensors in fine to moderate
spatial resolutions (Haack et al. 2014).
The longest, continuously functioning spaceborne mission has been Landsat with
eight successful launches since 1972. Optical systems, such as Landsat Thematic Mapper
(TM), passively record the surface reflectance of the Sun’s energy in the visible and infrared
spectral range. Of increasing interest to the remote sensing community are active sensors,
such as radar, that emit and receive wavelengths that are significantly longer than those
detected by optical systems. Radar can pass through atmospheric conditions, such as clouds,
that would obstruct the wavelengths of traditional spaceborne optical and multispectral
systems (Stefanski et al. 2014; Al-Tahir et al. 2014; Henderson et al. 2002). Radar can also
acquire data at night as it is not dependent upon the Sun for illumination. These benefits of
radar have important data collecting potential for many regions, especially those often
obscured by persistent cloudy conditions, such as tropical and high latitude regions (Sheoran
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& Haack 2013; Sawaya et al. 2010). In those locations, radar may be an important
independent source of information. Furthermore, as many studies have shown, radar can be
fused with optical data to improve land use/cover mapping accuracies (Hong et al. 2014; Hu
& Ban 2012; Pacifici et al. 2008; Waske & Van der Linden, 2008; Amarsaikhan et al. 2007;
Shupe et al. 2004;Chust et al. 2004; Kuplich et al. 2000; Haack et al. 2000; Haack & Bechdol
2000; Solberg et al. 1994).
For many applications with optical data, in particular applications involving green
vegetation such as agriculture, forestry, rangeland or wetlands, image date is often very
critical to the thematic accuracy of classifications. Scientists often collect detailed
information on phenology of natural vegetation and crop calendars to assist in the
identification of the best image date for acquisition (Van Niel &McVicar 2004). Having
multiple dates of imagery has also proven very effective in improving crop discrimination (Le
Hegarat-Mascle et al. 2000; Tso & Mather 1999; Turner & Congalton 1998). Multidate
analysis continues to be used by the United States Department of Agriculture, both
domestically and in the Foreign Agricultural Office, to improve the accuracy of their crop
inventories. The availability of free imagery, such as from the Landsat and other more recent
spaceborne missions, has made the compilation and acquisition of multitemporal image
datasets easier. Consequently, more research on multidate analysis can be found in the
literature on applications in many disciplines. Other remote sensing technologies such as
hyperspectral imaging are also becoming increasingly employed (Liu & Bo 2015; Gomez-
Chova et al. 2003; Camps-Valls et al. 2003).
Historically, the remote sensing community has placed greater emphasis on specific
date and multidate analysis using optical imagery. That decision was primarily based upon
having only single band and single polarization radar data where the information derived was
more on form or structure than composition. Given both the complexity and variety of land
use/cover types that exist, this presents challenges when attempting to extract unique
signatures for individual classes using data extracted for a single band (Dell’Acqua et al.
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2003; Toyra et al. 2001). Furthermore, compared to radar, optical data have been much more
widely accessible and for that reason, it is not surprising that it continues to be the dominant
source of imagery for most applications.
More recently, radar systems such as the Japanese Phased Array L-band Synthetic
Aperture Radar (Palsar), the Canadian RADARSAT-2, the German Aerospace Center/
European Aeronautic Defense and Space Company TerraRadar-X and the European Sentinel
sensors collect information from multiple polarizations. Importantly for the remote sensing
community, there is open access to the Sentinel radar data. These data may improve the
ability to extract more detailed surface information by different or multiple radar image dates.
Multidate or multitemporal remote sensing data sets have frequently provided better mapping
accuracies than single date images, especially for agriculture. There have been numerous
studies that have demonstrated improved crop accuracies with multidate radar or multidate
radar and optical integration (Bargiel & Herrmann, 2011; Blaes, Vanhalle & Defourny 2005;
De Wit & Clevers 2004). Yekkihkhay et al. (2014), for example, demonstrated while
studying agriculture in Canada that overall thematic accuracies increased by 14% using a
second date of radar and an additional 9% with a third date. That study evaluated the use of
multidate radar for more general land uses/covers than agriculture. Similarly, Niu and Ban
(2013) examined various land use/covers in an urban to rural fringe in the Greater Toronto
Area. Six image dates from Radarsat-2 were used, consisting of data collected from both
ascending and descending orbits. Kappa values of 0.91 were reported using all six image
dates compared to kappa values in the range of 0.51 to 0.67 using individual image dates.
Such studies continue to demonstrate the importance of investigating multiple image dates for
improving land use/cover mapping accuracies.
Although various studies have used multiple radar image dates to study land
use/cover, most of these studies have focused on comparing differences within the same
season and/or the same study area or for only one class (Bargiel & Herrmann 2014; Feng et
al. 2012; Hu & Ban 2012; Waske & Bruan 2009; Skriver 2008; Ban & Wu 2005; Engdahl et
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al. 2003; Shao et al. 2001; Pierce et al. 1998). Multiple image dates applied to different sites
provides the opportunity to test the transferability and robustness of varied methods and data
for extracting land use/cover in unique geographic areas. This is important since properties of
land use/cover often change with location because of differences in many human related
variables, such as demographic, technological, cultural and institutional factors.
Another issue with radar is that little attention has been given with respect to the
necessity or appropriateness of removing, or at least reducing the amount of speckle
(Maghsoudi et al. 2012; Bouchemakh et al. 2008; Lu et al. 1996). The amount of speckle
varies between radar data sets both as a function of the level of vendor preprocessing and
mode of acquisition, single or multi-look, with the first typically having more speckle
(Saevarsson et al. 2004). Decisions as to whether or not to despeckle the radar imagery before
more detailed analysis are important. In addition, it has been shown that combining original
radar and derived texture can improve mapping accuracies, at least for some classes (Idol et
al.2015b; Haack & Bechdol 2000), making derived texture measures a useful source of
information in surface classification.
The purpose of this study was to compare the differences in two seasons of Radarsat-2
imagery and the changes in classification accuracies for combining those seasons for two
sites. In this analysis both despeckled original data and derived texture measures were also
evaluated. The locations for these evaluations included an urban location, Washington, D.C.
USA and a site characterized by much more agrarian land use/cover, Wad Madani, Sudan. In
section 2 the study areas and data used are discussed. Section 3 contains the methodology
while in section 4 results are presented. Finally section 5 provides conclusions.
2. Study areas and data
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Two sites were selected for this analysis representing very different landscapes and climates.
The first is Washington, D.C. USA and the second Wad Madani, Sudan. Radarsat-2 quad-
polarized images at a nominal spatial resolution of 8 m were acquired. In addition to the
Radarsat imagery, ASTER optical images at 15 m spatial resolution were obtained for each
site to assist in identification of calibration and validation areas of interest (AOI).
Radarsat-2 C-band quad-polarization images were acquired over Washington, D.C. on
18 December 2008 and 17 July 2009. The December image is during the winter season when
many trees are in leaf-off condition and July is the summer leaf-on season. The Washington,
D.C. subset includes the major metropolitan complex and several surrounding suburban areas
(Figure 1). The imagery also includes a significant portion of forested areas (green and brown
tones). In addition, the Potomac River (black tones) provides the opportunity to classify water
bodies. The land use/cover classification features for Washington, D.C. consisted of urban,
forest, suburban and water (Figure 2) as described by Anderson et al. (1976). The vast
majority of high backscatter areas in Figure 1 are urban features located in and around
Washington, D.C. Most of these are suburban residential areas (pink tones) consisting of a
complex landscape of buildings, lawns, trees, roads etc. and creates a mix of high and low
radar returns. Governmental and commercial features (white tones) were mainly located in
downtown Washington, D.C.
INSERT FIGURE 1
INSERT FIGURE 2
Radarsat-2 data for Wad Madani were captured on 13 January 2009 during the winter,
fallow season and 6 June 2009 during the summer growing season (Figure 3). The 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 located on a bend on the west bank of the Blue
Nile River (white tones in Figure 3) and is approximately 160 km southeast of the Sudan’s
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capital city of Khartoum (Sawaya et al. 2010). The land to the west of the Blue Nile is
primarily agriculture (green tones). The land to the east of the Blue Nile and north of Wad
Madani is desert (dark tones). The Blue Nile crosses the middle of the image from South
Central to Northwest (bright green tones).
INSERT FIGURE 3
Consistent with Anderson et al. (1976), the following land use/cover features were
classified for Wad Madani: urban, fallow agriculture and/or bare ground, sparse natural
forest, water and agriculture (Figure 4). The width of the Blue Nile River fluctuates between
280 m and 460 m around the city of Wad Madani. Because of its narrow width, this water
body could introduce issues when using larger pixels for classifications or with some window
derived values. Also, and similar to Washington, D.C, the land use/cover classes extracted
were generalized and limited in number. However, for a comparison of the methods and data
used, these classes were considered acceptable for determining relative accuracies.
INSERT FIGURE 4
3. Methodology
Radar images for both dates were registered to a common geographic coordinate system,
UTM Zone 18 in the case of Washington, D.C. and UTM Zone 36 in the case of Wad
Madani. In both cases, an earth model of WGS 1984 was used. Next, land use/cover
classification was done following a three-stage approach. These stages as suggested by Foody
(1999) are training, classification and testing or accuracy assessment. During the training
stage, calibration or training Area of Interests (AOIs) were determined by knowledge of the
area and from visual analysis of the the ASTER imagery and from various other remote
sensing data, including image scenes from Google Earth.
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For the classification, the maximum likelihood classifier (MLC) was used. MLC is the
most widely adopted parametric classification algorithm for land use/cover mapping (Bailly
et al. 2007; Jensen 2005; Currit 2005; Weng 2002). In addition, its simplicity and wide
implementation in most remote sensing software packages, such as the ERDAS Imagine
software used in this study, were understandable factors in employing this algorithm for
classification. The combined quad-polarized bands (HH, HV, VH and VV) for the radar
images for each site (Section 4.1), for two seasons together (Section 4.2) and for both
extracted texture and in combination with the original radar (Section 4.3) were layer stacked
to create composite images for classification.
Finally, during the testing stage, thematic accuracies for each land use/cover class and
overall accuracies were assessed by comparison of a sample of the classified data to
validation or truth sites which were separate from the calibration locations. A classification
accuracy of 85% was used to suggest good class and overall thematic accuracy as
recommended by Congalton & Green (1999) and Anderson et al. (1976). For both calibration
and validation, two to four AOIs were selected for each class, containing about 1600 pixels
on average. It could be argued that point would be better than polygon validation samples
since polygons represent discrete generalizations of land use/cover classes (Verbyla &
Hammond 1995; Moisen et al. 1994; Janssen &Vanderwel 1994). This leads to issues
impacting multivariate normality or impurity of land use/cover classes (Richards & Jia 2005),
resulting in accuracy estimates, which are systematically different than the actual estimates
(Wulder et al. 2006). There are both advantages and limitations in each sampling approach,
point or polygon, some of which are addressed in Stehman & Czaplewski (1998) and with
either method used containing some level of inherent error (Congalton 1991). However, a
polygon sampling approach was selected for this study. Special attention was however taken
to select pure polygons to help overcome issues with class impurities. In addition, because
this research investigates relative thematic accuracy in comparison to absolute accuracy, the
use of polygons for validation was considered sufficient.
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In evaluating the differences in mapping accuracies between image dates and
combinations of dates, it is critical that coinciding land use/covers in both sets of images
remains unchanged. For example, if an area consists of forests at time T1 and at time T2 this
land use/cover was converted to urban land, this will lead to misleading or erroneous
classification results. Any new image generated from the fusion of two or more image dates
must therefore consider this issue before proceeding to use the new image for any further
analysis. To address this problem, both sets of images were carefully examined, reviewing
both calibration and validation sites, along with examining other areas where change may be
more susceptible, such as the periphery of city areas, for both image dates and for both study
areas. This was done by visually examining both sets of images directly and by use of
ancillary data from sources such as ASTER, Landsat and Google Earth. The results of this
qualitative assessment assured that both image dates for Wad Madani and Washington, D.C.
were acceptable to be fused. The next section presents the results of the various
classifications beginning with an independent classification of the original radar compared to
despeckled radar for independent seasons, followed by combined season images and then
progressing to texture evaluations and data combinations.
4. Results
4.1.Independent seasons
In this study, an initial comparison was made between the spectral signatures and thematic
classifications of the original and despeckled radar. Two window sizes were investigated, 3x3
and 5x5 using a Lee-Sigma algorithm. Comparison of both original and despeckled radar for
Washington, D.C. and Wad Madani showed a similar small increase in the range of 3% to 6%
in overall thematic accuracy, moving from the original to the despeckled radar with increased
window size. This was expected, particularly given the use of polygons for accuracy
assessment since despeckling is basically a smoothing filter. For Washington, D.C., the
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overall thematic accuracies for the original radar, despeckled 3x3 and despeckled 5x5 data
were 57% and 56%, 60% and 58%, and 62% and 59% for the December and July images
respectfully. The overall thematic accuracies for Wad Madani were 46% and 51%, 49%
and54%, and 51% and 57% for the January and June image scenes. The results for the 5x5
despeckled radar by location and season are in Tables 1 and 2. The overall thematic accuracy
is the lower right number in bold in each Table.
INSERT TABLE 1
INSERT TABLE 2
For Washington, D.C. the highest accuracies, user and producer, were observed for
water. Compared to other classes, water is much more easily detected by a relatively flat and
low return in radar imagery (Idol et al. 2015b; Jaroszewski & Lefevre 1998). This is evident
from Figure 1 and helps to explain the wide and continued use of radar in flooding
applications and in monitoring inland water reservoirs. The highest sources of confusion were
between forest and suburban and between suburban and urban classes. Green and scattered
vegetation along many suburban districts may give a similar appearance in radar backscatter
to fragmented or thinning forests. In an urban-rural interface, for example, around a large
city, land use/cover classifications using remote sensing continues to be difficult. The built
environments at these locations often share similar characteristics, which complicate the
selection of unique signatures. Furthermore, comparison of results for Washington D.C. by
class, both user and producer, and in overall thematic accuracy for both seasons had minimal
differences, about 3% with the winter, leaf off season having slightly better results. This may
be in part due to less classification ambiguity occurring between forest and suburban classes
during this leaf off period.
For Wad Madani, there was considerable confusion between the water and bare soil
classes. The highest producer’s accuracy for water was 93% with a much lower user’s
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accuracy of 59% in June and slightly improved for January (66%). Given the small width of
the Blue Nile, the larger window size in speckle reduction may have influenced these results,
along with an increase in the volume of water in the Blue Nile during January, making water
much more discernable in radar imagery during this time. Confusion was also evident in the
very low producer’s accuracy for bare soil, likely because the water and bare soil are both
acting as specular reflectors with similar low backscatter.
The sparse trees producer’s accuracy was low for both image seasons for Wad
Madani, 19% and 39% for June and January respectfully. Sparse trees were confused with
bare soil, agriculture and urban. The agriculture classification producer’s accuracy was low in
January (29%) with a great deal of confusion between the agriculture, bare soil and sparse
trees. This is understandable in that it was the fallow, winter season. The June improvement
in overall thematic accuracy by 6%, from 51% in January to 57%, is also reasonable as there
would be more separability in features because of the active vegetative growth, especially for
agriculture, during this season.
4.2. Combining seasonal radar images
As few as two images taken in different seasons can improve classifications when using
Landsat TM images (Guerschman et al. 2003). It has also been shown that combining wet and
dry season radar images can improve classification results (Villiger 2008). This has largely
been due to seasonal fluctuations influencing the separability between land use/cover types
(Vogelmann 2001). The combination of multiple image dates could then potentially lead to
improved classification accuracies, both overall and for individual classes. Each of the two
study sites had imagery that were acquired in both the winter and summer seasons. The two
seasonal images were layer stacked for each site, the combined images classified and their
respective error matrices generated (Table 3). As reported in Section 4.1, there was an overall
increase in classification accuracy in using the 5x5 despeckled radar imagery, when
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compared to the original radar and 3x3 despeckled data. The decision was therefore made to
use the 5x5 despeckled data when combining image dates for both the Washington, D.C. and
Wad Madani sites.
INSERT TABLE 3
The Washington, D.C. overall accuracy improved very little with the merging of the
winter and summer season images. The overall accuracy of the individual December and July
images were 62% and 59%, with the combination of both seasonal images increasing the
accuracy to 64%. Improvement in using the combined image was however better for the
producer’s accuracy for both the suburban and urban classes when compared to either of the
single seasons alone, with increases of 5% - 10%. In contrast, the forest and water classes for
each site showed a small decrease in producer’s accuracy of 2% - 8% in the combined image
classification.
The combination of the Wad Madani winter and spring season imagery helped to
improve most of the individual class results when compared to the results from either of the
two images independently. The combined image resulted in improved producer’s accuracy in
the bare soil (26%), sparse trees (7%), agriculture (37%) and urban (19%) classes when
compared to the best results obtained for the individual seasons. Whereas for the water class,
a small decrease of 3% in producer’s accuracy was observed in the combined imagery.
Combining the two different Wad Madani seasons provided an improvement of 8%
when compared to the best single season imagery results. The larger increase in overall
accuracy for Wad Madani was expected compared to the relatively small increase for the
Washington site because in Sudan there were more classes sensitive to seasonality. The built
up areas, however, provided better classification results when using the combined season
images than did the natural vegetated areas. This was unexpected, as impervious areas should
not change much by season. Rather, more diverse information should be available on plant
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life between seasons such as trees and agriculture areas in the combination of seasonal
images and leading to improved classification results.
4.3. Texture analysis and combination with original radar
For this study, variance texture measures were extracted for four different window sizes for
each band of the original, not despeckeled, Radarsat-2 data. The window sizes were 5x5, 9x9,
13x13 and 17x17. The use of variance texture was guided by the results of previous work,
suggesting this to be a suitable measure for extracting land use/cover from radar imagery
(Herold et al. 2004; Haack & Bechdol 2000). Also, it has been shown by Ulby et al. (1990)
that most texture measures extracted from the grey-level co-occurrence matrix are correlated,
further supporting the use of the variance measure in this study. The error matrices for the
best texture window size by season that were created using the original Radarsat-2 images are
contained in Tables 4 and 5. For all but one derived texture data set, the 17 x 17 window
provided the best results. The one exception was the July Washington image where the 13x13
window was slightly higher in overall accuracy. Figure 5 is an example of a classified texture
map for Wad Madani.
INSERT TABLE 4
INSERT TABLE 5
INSERT FIGURE 5
For both sites, the derived texture values significantly increase overall thematic
accuracies from the original radar. For Washington, D.C, those increases were about 10% and
for Wad Madani about 20%. However, the relative seasonal results were similar to the
original radar in both locations. For some individual classes, texture resulted in improved
producer’s accuracies while decreasing others. For Washington, D.C, texture improved
producer’s accuracies for forest and urban by 26% and 42% respectfully, while decreasing
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suburban by 24%. In Wad Madani, producer’s accuracy increased for sparse trees and urban
by 28% and 37%, with agriculture decreasing by 3%.
This analysis further took both of the 5x5 despeckled original radar images and
combined them with the best of the texture measures that was generated for that specific
image for both seasons. For Washington, D.C., this was the 17x17 and 13x13 texture
measures for the December and July images, and the 17x17 texture measures for both image
dates in the case of Wad Madani. Table 6 contains the results of these combinations of
despeckled and texture for the two seasons. Neither of these multiple season original and
texture combinations improved over earlier classifications for Washington, D.C., with the
best single date texture of 72% compared to the 73% when combined. Wad Madani, on the
other hand, improved from its best texture measure of 78% to 82%.
INSERT TABLE 6
5. Conclusions
There is an increasing availability of spaceborne radar to the basic science and application
remote sensing communities. The European Space Agency, for example, under its
Corpernicus program has a series of missions planned collectively known as the Sentinel
program. A series of six satellites will be launched into orbit collecting radar data at various
spatial resolutions, swaths and polarizations, providing global coverage of the earth surface.
Already, the first two missions Sentinel-1A and Sentinel-2A have been deployed in April
2014 and June 2015 respectively, capturing information free for civilian use (ESA, 2014).
Similarly, the Alaska Satellite Facility continues to share much of its radar data with the
science community. More recently, the Japanese Aerospace Exploration Agency in
November 2014 has made available four annual global Palsar datasets for the years 2007 to
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2010, also free for use (JAXA, 2014). Many more similar contributions are expected in the
future with the expansion and deployment of more operational spaceborne radar programs.
Given the increasing availability of radar data, it is important to understand both the
strengths and weaknesses of using radar for land use/cover classifications. Optical imagery
generally provides better classifications than radar but in many parts of the world, such as the
tropics and high latitudes, it is difficult to collect optical imagery without extensive amounts
of clouds. These locations where optical imagery is unavailable will increasingly use
spaceborne radar. Multidate, quad-polarization and derived radar values such as texture can
all contribute to improved mapping accuracies based only on radar alone as this study has
demonstrated.
This study compared classifications of different seasons of radar and the combination
of seasons for two study locations. Despeckled Radarsat-2 and derived texture measures were
both included in this analysis. There were minimal differences between seasons in thematic
accuracies, 3% in Washington, D.C. and 6% in Sudan. Interestingly, but not surprising, given
that Washington is an urban/suburban landscape and Sudan is agricultural, the leaf off image
was better for Washington, D.C., while the growing season gave better classification results
for Sudan. Combining the two dates increased results by 2% for Washington and 9% for
Sudan. This suggests that different surface features will respond better to multidate analysis
than others. There is less reason to believe that urban classification will vary seasonally but
agriculture and natural vegetation would be expected to be very responsive to image date and
multidate analysis. It is also very likely that different image dates and more than two dates
might further lead to improved classifications for both user and producer, and overall
thematic accuracies.
As with many studies using radar to map land use/cover, derived texture measures
greatly improved results (Idol et al. 2015a; Sawaya et al. 2010), but did not vary the results
for the individual and combined seasons. The combination of both dates of despeckled radar
and texture had very little improvement on the best texture measure. The result for this
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combination for Washington, D.C. and Wad Madani were 78% and 82% respectively,
representing a good classification for a radar only derived land use/cover map.
In the future, further investigation will evaluate various other classifiers (e.g. support
vector machines and neural networks) and image dates, the combining of radar backscattering
coefficients, polarimetric parameters and textural features for classification, along with an
assessment of different texture measures for improving both class specific and overall
thematic accuracies. Additionally, and moving from relative to absolute accuracies, an
evaluation of the sampling process including the choice of point versus polygon validation
samples, the sample design (e.g. random versus stratified) and the impact of these various
issues on multi-date radar imagery will be investigated.
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Acknowledgements
The authors would 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 project 3126 of the Science and Operational Application
Research for RADARSAT-2 program. 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 by the Department of Geography and Geoinformation Science at George Mason
University.
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Conflicts of Interest
The authors declare no conflict of interest.
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Figure 1. Radarsat-2 composite (HH, VV and VH in RGB) image over Washington, D.C.
Image footprint is approximately 27 x 32 km and was collected on 17 July 2009.
(top left image) Forested , (top right image) Suburban, (bottom left image) Urban, (bottom
right image) Water
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Figure 2. Optical scenes of Washington D.C. classes from Aster imagery
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Figure 3. Palsar composite (HH, VV and HV in BGR) image for Wad Madani.
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Figure 4. Optical scenes of Wad Madani classes from Aster imagery.
(top left image) Agriculture,(top right image) Sparse trees, (middle left image) Bare soil,
(middle right image) Urban, (bottom image) water
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Figure 5. Classification occurring over Wad Madani. Classification completed using
Radarsat-2 January Texture measure 17x17
(water – blue, agriculture – light green, bare soil – gray, sparse trees – dark green, urban –
red)
Approximate scene width 22 x 21 km
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Tables
Table 1. Error matrices for Washington classification using despeckled 5 x 5 window
Washington, D.C. - Radarsat 2 - December image accuracies in
%
Water Forest Suburban Urban User accuracy
Water 4782 0 2 29 99
Forest 0 2597 1658 744 52
Suburban 86 1777 2398 1963 38
Urban 101 519 528 2403 67
Producer accuracy 96 53 52 46 62
Washington, D.C. - Radarsat 2 - July image accuracies
in %
Water Forest Suburban Urban User accuracy
Water 4101 1 2 3 99
Forest 0 2365 1661 777 49
Suburban 598 2145 2507 1785 35
Urban 270 382 416 2574 70
Producer accuracy 82 48 54 50 59
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Table 2. Error matrices for Wad Madani classification using despeckled 5 x 5 window
Wad Madani - Radarsat 2 - January image accuracies in %
Water Bare Soil Sparse Trees Agriculture Urban User accuracy
Water 14822 7081 230 68 107 66
Bare Soil 1692 4774 2481 1285 1109 42
Sparse Trees 108 255 10073 8592 4767 42
Agriculture 10 8 3351 5566 5229 39
Urban 2 12 1879 3286 8838 63
Producer accuracy 89 39 55 29 44 51
Wad Madani - Radarsat 2 - June image accuracies in %
Water Bare Soil Sparse Trees Agriculture Urban User accuracy
Water 15486 9558 203 572 125 59
Bare Soil 1145 2409 1266 2621 632 29
Sparse Trees 1 18 9434 3100 4179 56
Agriculture 2 145 5390 11777 4636 53
Urban 0 0 1721 727 10478 81
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Producer accuracy 93 19 52 62 52 57
Table 3. Error matrices of two combined seasons
Washington, D.C. - Radarsat 2 - Despeckled 5x5 merged seasons accuracies in %
Water Forest Suburban Urban User accuracy
Water 4350 0 0 0 10
Forest 0 2483 1404 431 58
Suburban 414 2069 2725 1825 39
Urban 205 341 457 2883 74
Producer accuracy 88 51 59 56 64
Wad Madani - Radarsat 2 - Despeckled 5x5 merged seasons accuracies in %
Water Bare Soil Sparse Trees Agriculture Urban User accuracy
Water 14993 6618 7 2 4 69
Bare Soil 1624 5447 570 650 169 64
Sparse Trees 11 39 10608 3913 3318 59
Agriculture 6 26 5149 12697 3939 58
Urban 0 0 1680 1535 12620 80
Producer accuracy 90 45 59 68 63 66
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Table 4. Washington, D.C. error matrices of Radarsat 2 variance texture measures
December texture window 17x17 accuracies in %
Water Forest Suburban Urban User accuracy
Water 4897 0 0 0 100
Forest 0 3430 2242 7 60
Suburban 70 1233 1160 546 39
Urban 2 230 1184 4586 76
Producer accuracy 99 70 25 89 72
July texture window 13x13 accuracies in %
Water Forest Suburban Urban User accuracy
Water 4215 0 0 0 100
Forest 0 3882 2300 94 62
Suburban 754 876 1396 673 38
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Urban 0 135 890 4372 81
Producer accuracy 85 79 30 85 71
Table 5. Wad Madani error matrices of Radarsat 2 variance texture measures
January texture window 17x17 accuracies in %
Water Bare Soil Sparse Trees Agriculture Urban User accuracy
Water 14697 6011 0 0 0 71
Bare Soil 1619 6044 316 87 0 75
Sparse Trees 226 58 14468 9745 577 58
Agriculture 92 17 2691 7164 2067 60
Urban 0 0 539 1801 17406 88
Producer accuracy 88 50 80 38 87 70
June texture window 17x17 accuracies in %
Water Bare Soil Sparse Trees Agriculture Urban User accuracy
Water 15481 7595 0 0 0 67
Bare Soil 1072 4493 29 2029 0 59
Sparse Trees 31 0 17075 5749 1497 70
Agriculture 50 42 523 10989 32 94
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Urban 0 0 387 30 18521 98
Producer accuracy 93 37 95 59 92 78
Table 6. Dual seasons original despeckled and texture
Washingon, D.C. accuracies in %
Water Forest Suburban Urban User accuracy
Water 4418 0 0 0 100
Forest 0 3501 1700 0 67
Suburban 416 1167 1615 382 45
Urban 135 225 1271 4757 75
Producer accuracy 89 72 35 93 73
Wad Madani accuracies in %
Water Bare Soil Sparse Trees Agriculture Urban User accuracy
Water 15155 5849 0 0 0 72
Bare Soil 1398 6278 31 544 0 76
Sparse Trees 66 3 16881 5300 1203 72
Agriculture 15 0 556 12891 56 95
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Urban 0 0 546 62 18791 97
Producer accuracy 91 52 94 69 94 82
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