URC: Unsupervised regional clustering of remote sensing imagery

Conditional random fields (CRF) have been used extensively for spatially coherent segmentation and classification of images. As a result, may techniques for finding the optimal inference of CRFs have been developed. However, CRFs have seen little use in clustering and segmenting of satellite imagery due to the large number of pixels in satellite images. In this paper we present a means of defining remote sensing imagery as a region based conditional random field (CRF). Unlike the pixel based CRF, our region based CRF can be optimally solved using the latest advancements in CRF inference techniques because the region based CRF is a computationally simpler problem to tackle. To reduce the loss of information when forming the region based CRF, the regions are defined using a superpixel algorithm which decreases the spatial resolution while preserving the original satellite image´s structure. Furthermore, unlike previous approaches, we show that the optimal number of clusters in the satellite images can be automatically determined by formulating the number of clusters as part of the CRF cost function. This unsupervised region based approach is a non-parametric formulation which is validated using different imaging modalities: SAR and hyper-spectral imaging.