A GPU Accelerated Contourlet Method for Detecting Changes Due to Fire Using Remote Sensing

Remotely sensed images are suitable resources to examine the extent and impact of fire on the land-scape. This paper presents a multiresolution approach to detect changes due to fire. A large number of change detection methods utilizing wavelet features have been developed. However, usage of contourlets is limited in context of change detection application. The aim of this paper is to investigate and propose a texture based model utilizing contourlets in parallel-processing framework to detect changes due to fire. To achieve near real time processing, the proposed algorithm is implemented on NVIDIA's Graphical Processing Unit. The results are compared with the changes detected by different thresholding methods in the feature space. It is found that the proposed method exhibits high change detection accuracy with better edge continuity. The results show that the GPU implementation improves the performance in terms of execution time as compared to the sequential implementation.