Segmentation of High-resolution Satellite Imagery Based on Feature Combination

High resolution (H-res) satellite sensors provide rich structural or spatial information of image objects. But few researchers study the feature extraction method of H-res satellite images and its application. This paper presents a very simple yet efficient feature extraction method that considers the cross band relations of multi-spectral images. The texture feature of a region is the joint distributions of two texture labelled images that are calculated by its first two principal components (PCs) and the spectral feature is that of grayscale pixel values of its two PCs. The texture distributions operated by a rotation invariant form of local binary patterns (LBP) and spectral distributions are adaptively combined into coarse-to-fine segmentation based on integrated multiple features (SIMF). The performance of the feature extraction approach is evaluated with segmentation of H-res multi-spectral satellite imagery by the SIMF approach.