Quad-tree based compressed histogram attribute profiles for classification of very high resolution images

This paper presents a novel quad-tree based compressed histogram attribute profile (QT-CHAP) for classification of very high resolution remote sensing images. The QT-CHAP characterizes the marginal local distribution of attribute filter responses to model the spatial context of each sample with a very small number of image features. This is achieved based on a three steps algorithm that comprises a novel non-uniform quantization strategy to the compression of the information present in standard histogram attribute profiles. Due to the proposed quad-tree based non-uniform quantization strategy, the proposed QT-CHAP results in an optimized tradeoff between information extraction and number of considered features. Experimental results confirm the effectiveness of the proposed QT-CHAP in terms of computational complexity, storage requirements and classification accuracy when compared to the other state of the art attribute profile based methods.