The change-detection map produced by IR-MAD in this paper is a coarse detection map and needs further manual checks to improve the accuracy of the product. In this section, the four images of
Figure 18 are tested. The image in
Figure 23I is the change-detection map between 11 April and 27 April, in which clouds and shadows are considered as the change, and
Figure 23II is the change-detection map after removing the cloud and shadow regions based on the
Figure 23I image.
Figure 23A–D give the detailed enlarged images of the red rectangle in the large maps.
Figure 23A illustrates the change of open grass land before and after earthquake, where some tents have been put up as temporary refuge.
Figure 23B indicates the landmark building of the Kathmandu, Bhimsen Tower, completely collapsed after the earthquake.
Figure 23C represents an error change detection result, as the different imaging angle of two images result in the different positions of tall buildings and their shadows in the images. Though the images are matched by the method proposed in
Section 2.2, the differences caused by the viewing angle is very difficult to be corrected.
Figure 23D also shows an error change-detection result, because part of the shadow area is missing from detection. From the above analysis, the change-detection map produced by our proposed procedure is a coarse result without distinguishing the type and grade of change and many errors are detected, which require manual checking to refine the work.
The image in
Figure 24I is the same image shown in
Figure 23II, which is the change map between 11 April and 27 April. The image in
Figure 24II is the change between 1 May and 11 April and the image in
Figure 24III is the change between 27 April and 1 May. All of these eliminate the impact of cloud and shadow.
Figure 25 shows the detailed enlarged images of the red rectangles of
Figure 24. In
Figure 25A, the Durbar Square of Kathmandu, which damaged severely in the earthquake, is shown, but this change (the damage) is not discovered because the clouds and shadows exist in the image of 27 April (seen in
Figure 25A). In
Figure 24III, the change is observed, since the image of 1 May is cloudless in this region. For B sense in the
Figure 24, the reason of discovering the change in II, not in I and III map is the same with the A sense as the influence of shadow (seen the detail in
Figure 25B).
Figure 25C shows the Kal Mochan Temple of Kathmandu, which also collapsed after the earthquake. In
Figure 24I,II, the change area is the same large, but small in
Figure 24III map. This is because the feature on the ground has no major changes in the post-earthquake images (27 April and 1 May), while the feature changed obviously between pre- and post-earthquake images (
Figure 24I is the result of 11 April and 27 April,
Figure 24II is the result of 11 April and 1 May). There are just some rocks from the collapsed temple that have been consolidated during the period from 27 April to 1 May to clean the area of the ruins (shown in
Figure 25C). By these comparisons, the progress of the rescue will be learned. The same reason applies to the
Figure 25D,E scenes. From these images, we see that many tents were put up in some open space nearby the houses of victims as temporary shelters because of the constant large aftershocks. The temporary shelters were migrated or revoked on 1 May as the rescue proceeded. So some temporary facilities would disappear on the image of 1 May (seen in
Figure 25D,E); and no or few change areas would be detected in image III of
Figure 24. The results of this paper mainly provide a quick change detection map to determine the urgent requirements, as well as offer guidance to check the change regions precisely through a community remote sensing platform for experts or the public, which is possible to reduce labor costs and improve efficiency in the maximum extent.
Figure 26 show the detected changes vector superposition on cloudless maps of 27 April. The vector of the left hand map is the final result, which was extracted from the image I of
Figure 26; and the vector of the right hand map removes the error regions of the final result by visual checking. The right vector map is the combination of visual interpretation by four independent people. This vector map can be considered the relative correct result for comparing. The simple statistic is shown in
Table 6. The number of change regions in the original vector map (shown in
Figure 26 left hand map) is 758, while the number in the checked vector map (shown in
Figure 26 right hand map) is 556, and the error detected change regions are 202. The percentage of truth change regions and original change detected regions is 73.4%. This can meet the application requirements to a certain degree. In the true change regions, most of them are tents or shelters (shown
Figure 23A,
Figure 25D,E, and
Figure 27A), some are cars on the street or other temporary features, and a few are the collapsed or damaged buildings (shown in
Figure 25C and
Figure 27B). In the error change regions, as analyzed above, most of the error is caused by the changes in different view angles between pre-event maps and post-event maps, and some are caused by undetected clouds, thin clouds, shadows, or other unknown errors.
Through the change detection map, the rescue progress can be observed. An example is shown in
Figure 27. The image in
Figure 27A shows the difference over four days in the open space of a park in the center of Kathmandu. The image from 27 April, the day after earthquake, indicates that the situation in this open space is in chaos, with a variety of temporary residences set up. However, by the sixth day after the earthquake, on the 1 May, the temporary settlements are orderly. This phenomenon is more obvious and more and unified residences and relief tents are set up by 2 May. The image in
Figure 27B shows the series of changes of Bhimsen Tower. From 27 April to 2 May, the bricks and masonry from the ruins are cleaned up effectively and the situation changed from one of chaos to order. These temporal change maps indicate the progress of the disaster rescue, allowing for more objective guidance to formulate the next plan of rescue and allocate rescue resources reasonably.