Unsupervised classification of agricultural land cover using polarimetric synthetic aperture radar via a sparse texture dictionary model

A sparse texture dictionary learning method for unsupervised land cover classification is presented. The method takes the stance that land cover in remote sensing data is best analysed in texture patches rather than localized pixels. To this end, a feature vector is designed that describes local texture information in a spatially coherent manner. This texture model is extracted for each pixel in the scene. A sparse dictionary of global texture models is then learned to characterize the underlying texture distribution of the scene in a simplified manner. An unsupervised classifier is learned using these global texture models for grouping pixels exhibiting high similarity. Being an unsupervised classifier, the class labels that are learned are unbiased toward human interpretation of the scene, and rather are learned according to the texture information. The method is validated using polarimetric SAR data over a Flevoland, Netherlands agriculture scene, but may be generalized to any remote sensing data. Promising experimental results show how the proposed method retains the spatial coherence of crops, and attains higher accuracy than recent unsupervised and supervised classification methods using the same data.