Double random field models for remote sensing image segmentation

Abstract

By incorporating the local statistics of an image, a semi-causal non-stationary autoregressive random field can be applied to a non-stationary image for segmentation. Because this non-stationary random field can provide a better description of the image texture than the stationary one, an image can be better segmented. Besides low-order dependence among pixels in image for above-mentioned texture random field, the paper also introduces high-order dependence as a new classification feature to recognize the real object. Entropy rate that depicts the high-order dependence feature can also be estimated by using random field model. The proposed technique is applied to extract urban areas from a Landsat image.