| Abstract: | Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards. Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data. However, the existing subpixel-based change detection(SCD) models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions. To overcome such issues, a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis(CVA) and hence, named as a subpixel-based change vector analysis(SCVA). This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor(AWiFS) and Landsat-8 datasets. To check the effectiveness of the proposed SCVA, the cross-validation of the results has been done with the existing neural network-based SCD(NN-SCD) and per-pixel based models such as fuzzybasedCVA(FCVA)andpost-classification comparison(PCC). The results have shown that SCVA offered robust performance(85.6%-86.4%) as comparedtoNN-SCD(81.6%-82.4%),PCC(79.2%-80.4%), and FCVA(81.2%-83.6%). We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps. This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets. |
