Texture retrieval from very high resolution remote sensing images using local extrema-based descriptors

This paper proposes a novel approach for texture-based image indexing and retrieval in the scope of very high resolution (VHR) optical imagery. Our motivation is to take into account local textural features and structures inside each image to measure its similarity to other images. These local features are extracted for a set of characteristic points from the image using the local extrema-based descriptors (LED) from which the radiometric, spatial and gradient features of the local extrema pixels (i.e. maximums and minimums) are integrated to characterize local textures. Due to the fact that VHR images usually involve a variety of local textures which may weakly verify the stationarity hypothesis, an approach based on characteristic points like extrema pixels becomes relevant and effective. We perform our experimentation using texture databases extracted from VHR Pleiades images within the application of vineyard cultivation and oyster farming study. Retrieval results yielded by the proposed strategy are very promising and competitive compared to reference methods.