Land use classification with biologically inspired color descriptor and sparse coding spatial pyramid matching
Land use classification using remote sensing images plays a key role in many applications such as urban mapping and geospatial object detection. With the rapid development of satellite sensors, high resolution images which exhibit more detailed textures now can be acquired. How to effectively represent these images and recognize the categories of land use/land cover scenes has become a challenging task. In this paper, we propose a novel biologically inspired descriptor combined with the sparse coding spatial pyramid matching (ScSPM) for land use classification. A color processing pipeline is first presented to simulate the opponent responses of human visual system. By extending the scale invariant feature transform (SIFT) on processed color channels, a descriptor that is able to jointly extract color and shape information for land use images is proposed. Then the ScSPM model is employed to incorporate the local descriptors of an image, followed by a linear kernel support vector machine (SVM) for image classification. Performance evaluation on the publicly available LULC data set demonstrates that the proposed method achieves better classification accuracy than other reference methods.
Multimedia Tools and Applications