: Use of Sigma 0 and radar images to extract and classify roads in Central Africa Progress Reports Study supported by the Canadian Space Agency
: Use of Sigma 0 and radar images to extract and classify roads in Central Africa I-Introduction This study aims to determine whether RADARSAT images can be used for the automated detection of roads in a forested region in Central Africa with the Sigmaroutes algorithm. This algorithm, originally developed for the updating of 1 :20 000 topographic maps produced by the Quebec government, will be tested on radar images acquired over two test sites in the Republic of Congo. Sigma-routes uses as input the segments of existing roads to help guide the search for new road segments. Fine mode RADARSAT-1 images, with a spatial resolution of 8 m, were tested with roads that appear to be 2 to 3 pixels wide. IV-Study area and available data Two forested areas were identified as test sites by the World Resource Institute, one in the North of the country and one in the South (see figure 1). Figure 1. Location of study area North study area South study area Upper left : Upper left : (643561 Easting; 220388 Northing) in UTM zone 33 D000 (108066 Easting; 396973 Northing) in UTM zone 33 D000 (Long 16 17'26.9" E; Lat 1 58'39.7" N) (Long 11 28'23.4" E; Lat 3 35'04.7874" S) Lower right : Lower right : (674447 Easting; 185673 Northing) in UTM zone 33 D000 (145195 E ; 453690 S) in UTM zone 33 D000 (Long 16 34 05.4" E ; Lat 1 39 54.4" N) (Long 11 48'17.9" E ; Lat 4 05'53.5" S) CSA Contract no. 28/7006217 1
A total of 15 images were acquired during the spring and summer of 2008. Their characteristics are provided in table 1. South region North region Date Orbit Angle Date Orbit Angle 17 May 2008 Ascending F2F 18 May 2008 Descending F2 21 May 2008 Descending F3F 04 June 2008 Ascending F2N 10 June 2008 Ascending F2F 11 June 2008 Descending F2 14 June 2008 Descending F3F 28 June 2008 Ascending F2F 04 July 2008 Ascending F2F 05 July 2008 Descending F2 08 July 2008 Descending F3F 22 July 2008 Ascending F2N 28 July 2008 Ascending F2 29 July 2008 Descending F2 01 Aug 2008 Descending F3F Table 1. List of RADARSAT-1 images acquired for the study Figure 2. Road and hydrographic networks in Congo and location of the two test areas CSA Contract no. 28/7006217 2
V- Data preprocessing V-2- Image preprocessing Preprocessing of the radar images includes both radiometric and geometric calibrations. These were carried out using specialized functions of the PCI Geomatica software (CDSAR, SARINCD and SARSIGM). SARSIGM calculates the calibrated backscatter parameter σ 0. These 32-bit values are then redistributed over 8 bits in order to enable their processing using standard image processing algorithms (figure 3). Figure 3. Distribution of 32-bit calibrated RADARSAT-1 images (σ 0 ) and of values redistributed in 8 bits V-3- Vector data The vector databases were downloaded from the Global Forest Watch Web site (http://atlas.globalforestwatch.org/congo/default.aspx). They contain levels 2 and 3 road and hydrographic networks. The vectors were available in UTM 32 S E012 projection and thus needed to be reprojected in the RADARSAT image projection : UTM 33 D000. The vectors were rasterized so that they could be processed with the radar images, using Sigma-routes. V-4- Radar image orthorectification The radar images were geometrically corrected using the road and river networks provided. In the North region, the road network is dense and has more or less straight lines that are easy to identify on the images (figure 4). However, the roads in the South are more irregular and thus more difficult to find on the images. CSA Contract no. 28/7006217 3
Uncorrected Georeferenced Figure 4. Example of geometric correction of a RADARSAT-1 image acquired 4 June 2008 (North) VI- Preliminary tests A technique that had yielded satisfactory results in past studies with images acquired over Quebec was first tested. It consists of using a single image that corresponds to the mean of three images in a given mode, either ascending or descending. This was tested with the ascending images of the North sector (4 June, 28 June and 22 July). This averaging technique is particularly interesting because it allows to significantly reduce radar speckle. However, if the geometric correction of the images is insufficiently precise, this will yield imperfect superposition of the road segments. This in turn may cause road segments to disappear in the mean image, something we want to avoid, for obvious reasons. CSA Contract no. 28/7006217 4
The area that is common to all 7 images acquired for the North region is shown in figure 5. Figure 5. Area that is common for all images acquired in the North sector Zooming in on the road segments of this region (figure 6) reveals that there exists a 3- to 4-pixel gap between the road as it appears on the image and its geographical position (database). We can also see that the road spans 4 to 5 pixels in width and are relatively long. One should note that the pixels were resampled to 5 meters. These observations led us to choose the following values for the parameters of the algorithm : - Filter size : 15*3 (FLSZ[1] * FLSZ[2] ; - Perpendicular offset for existing road detection : between 7 and 10 ; - Parallel offset for the detection of the 1st pixel of new road segment : 10 ; - Maximum pixel gap for the search of new roads : 5 ; - Minimal size for new road segments : 40 pixels; - Detection threshold (TVAL): to determine. CSA Contract no. 28/7006217 5
Figure 6. Lateral offset and segment width The results are expressed in terms of a classification of existing and new roads. Existing roads are classified in three categories: intact (most pixels are detected), disappeared (most pixels are not detected), and suspect (between the first two categories). Test no. 1 A small threshold was used (TVAL= 0.2) and a minimal road length of 40 pixels was used for new segments. With these values, most existing segments are detected (classified intact ) and many small new segments are labelled as possible false alarms. Values tested in Test no. 1 Results obtained (green : intact segments, yellow : suspect segments, red : disappeared, blue: new) Figure 7. Results obtained in Test no. 1 CSA Contract no. 28/7006217 6
Test no. 2 A larger threshold (TVAL=0.22) and a minimal road length of 50 pixels for new segments were used. Compared with test no. 1, we can see more suspect segments appearing and a much smaller number of false alarms with respect to new segments. Values tested in Test no. 2 Results obtained (green : intact segments, yellow : suspect segments, red : disappeared, blue: new) Figure 8. Results obtained in Test no. 2 Test no. 3 Here, filter size of 13 x 3 rather than 15 x 3 was used. Results are nearly identical to those of Test no. 2. Values tested in Test no. 3 Results obtained (green : intact segments, yellow : suspect segments, red : disappeared, blue: new) Figure 9. Results obtained in Test no. 3 CSA Contract no. 28/7006217 7
Test no. 4 A lower detection threshold and a minimal length of 100 pixels were used. A few possible new segments appear. For existing roads, perpendicular offset was reduced from 9 to 7, which resulted in the disappearance of road segments with a significant gap between their image position and that of the map. Values tested in Test no. 4 Results obtained (green : intact segments, yellow : suspect segments, red : disappeared, blue: new) Figure 10. Results obtained in Test no. 4 VI- Conclusions and perspectives These preliminary tests show there is some potential to detect roads in a tropical forest setting from RADARSAT images. In the next step, a more exhaustive series of tests will be carried out using individual images over both the North and South regions. In order to better evaluate the validity of the detection of new roads, we will carry out some simulations whereby some segments of the existing roads will be removed. This is found to be necessary because it appears that very few actual new roads can be detected, even on recent optical imagery, as was observed from Google Earth imagery of 2008. CSA Contract no. 28/7006217 8
Progress Report no. 2 : Use of Sigma 0 and radar images to extract and classify roads in Central Africa 1. Introduction Progress Report no. 2 In this report, we discuss the capacity of sigma 0 to detect new roads using the average of three RADARSAT-1 images in ascending mode in the North region of Congo. We also look at the South region which contains roads that appear to be narrower than the ones found in the North. 2. Tests with individual images North region In our previous report, we had carried out a few tests for the North region using a combination of values for the algorithm parameters. We showed that sigma 0 could relatively easily detect existing roads in that region. However, it was difficult to determine if it could detect new roads since the map that was made available for the study had been recently updated. For that reason, we decided to delete some road segments from the base map data and run the algorithm to see whether it could detect these deleted segments as new ones. We ran a test with a severe detection threshold (test no 4 in progress report no.1). This resulted in certain segments being less detectable than others. The former are termed suspect while the others are termed intact in figure 1. We deleted segments belonging to those two categories (circled in red in figure 1) and tested sigma 0 to see if it could detect new roads. The test was carried out on an image resulting from the averaging of three RADARSAT- 1 images in ascending mode (4 June, 28 June and 22 July 2008) and another image resulting from the averaging of four images in descending mode (18 May, 11 June, 5 July and 29 July 2008). The values used for the various parameters were: Filter size: 15x3 Detection threshold: 0.14 Minimal length for new segments: 100 pixels. CSA Contract no. 28/7006217 9
Progress Report no. 2 Figure 1. Segments that were deleted in order to test the capacity of sigma 0 to detect new roads. Filter size: 13x3; detection threshold: 0.22. The results are shown in figure 2. North-south segments were relatively well detected whereas east-west segments were not detected at all. The visibility of roads on a radar image depends on three factors: road width, surface roughness along the edges of the roads (presence or absence of trees) and road orientation with respect to radar beam orientation. The optical image in figure 2 shows that the undetected horizontal roads were not necessarily narrower than the vertical ones. Since RADARSAT s orbit is almost north-south (ascending or descending) and view angle is perpendicular to that direction, structures that are oriented along a north-south axis produce sufficient backscatter whereas those that are oriented along an east-west axis are much less visible. We can also see on figure 2 that the radar image contains several linear elements that could correspond to roads, linear hydrographic elements (irrigation or drainage channels), topographic elements or simply false alarms related to speckle. It would be interesting to carry out some field verification to determine what those linear features actually are. CSA Contract no. 28/7006217 10
Progress Report no. 2 CSA Contract no. 28/7006217 11
Progress Report no. 2 Figure 2. «New roads» detected (in yellow) on radar image and compared to reference optical image (source: Google Earth) In the case of the South region (figure 3) the road segments included in the base map are not visible at all. However other linear patterns are clearly visible. An example is given in Figure 4 where these patterns are drawn in yellow. It is evident that under these conditions sigma 0 is not applicable. CSA Contract no. 28/7006217 12
Progress Report no. 2 Figure 3. Linear patterns are visible on the RADARSAT-1 images of the South region but they do not correspond to the road segments of the base map. Figure 4. Linear patterns delineated in yellow on the radar images (South region) and road network according to basemap in red Possible explanation: In trying to explain why this difference occurs between the North and South regions with respect to road detectability, we checked the attributes table of the road network and found the following road types: - Road types in the North : main forest roads + secondary forest roads + a few exploitation roads. - Road types in the South : main forest roads + secondary forest roads In other words, road type as indicated in the database does not provide an explanation for this difference between the North and South regions with respect to road detectability. CSA Contract no. 28/7006217 13
Progress Report no. 2 Using Google Earth, however, and zooming in at the same scale in the North and South regions, it appears that roads in the South are less visible and thus most likely narrower and/or less important than the ones in the North (figures 6 and 7). Figure 5. Area of interest on Google Earth CSA Contract no. 28/7006217 14
Progress Report no. 2 Figure 6. Google Earth images of the South (top) and North (bottom) regions 3. Upcoming activities We will concentrate our efforts in the North region and carry out tests on individual images using different values for the algorithm parameters in order to determine optimal values for this type of environment. We will also attempt to determine the RADARSAT-1 viewing angle(s) that are optimal for road detection. CSA Contract no. 28/7006217 15