GPU Accelerated Textons and Dense SIFT Features for Human Settlement Detection from High-Resolution Satellite Imagery

Automated analysis of large-scale high-resolution satellite imagery requires computationally efficient image representation techniques that characterize the visual content of a scene. The computational process involved in feature descriptor generation is often expensive and its scalability to large image databases forms an important research problem. This paper presents an overview of our work on exploiting the Graphics Processing Unit architecture for careful implementation of two different feature representation techniques – (i) Textons and (ii) Dense Scale Invariant Feature Transform. We evaluate the performance of our implementation for human settlement detection on an image database consisting of high-resolution aerial scenes representing diverse settlements. The rapid computation and robust detection accuracy of our experiments suggest that this High Performance Computing based framework has unique capabilities for Peta-scale production of high fidelity human settlement maps.