High performance SIFT feature classification of VHR satellite imagery for disaster management

High resolution satellite imagery is useful for disaster management activities such as damage assessment, immediate delivery of relief assistance etc. The process of analyzing Satellite imagery involves extraction of optimal features that closely represent the damaged areas. Accuracy of the analysis depends on the efficiency and robustness of selected features. Scale invariant feature transform (SIFT) enables to extract features, which are scale and rotation invariant. It provides robust features even in cluttered and partially-occluded images (such as those images that are obtained from a post disaster scenario). SIFT is robust at the cost of multiple stages involved in making features scale and rotation invariant, which is a time intensive process to apply on high resolution imagery. In general, there is a need to synthesize large amount of high-resolution, high temporal satellite data for disaster management applications to enable near real time response. However, this task is computationally intensive. Hence, this work focuses on high performance robust SIFT based feature extraction of various earthquake affected areas from high resolution imagery and subsequent classification of using Support Vector Machines (SVM). The high performance computing frameowrk consists of Tesla C2075 Graphics processing unit (GPU) with 448 cores. Results obtained from GPU implementation is shows significant gains in computational time over CPU based approach.