Graph based hyperspectral image segmentation with improved affinity matrix

Image segmentation and clustering is a method to extract a set of components whose members are similar in some way. Instead of focusing on the consistencies of local image characteristics such as borders and regions in a perceptual way, the spectral graph theoretic approach is based on the eigenvectors of an affinity matrix; therefore it captures perceptually important non-local properties of an image. A typical spectral graph segmentation algorithm, normalized cuts, incorporates both the dissimilarity between groups and similarity within groups by capturing global consistency making the segmentation process more balanced and stable. For spectral graph partitioning, we create a graph-image representation wherein each pixel is taken as a graph node, and two pixels are connected by an edge based on certain similarity criteria. In most cases, nearby pixels are likely to be in the same region, therefore each pixel is connected to its spatial neighbors in the normalized cut algorithm. However, this ignores the difference between distinct groups or the similarity within a group. A hyperspectral image contains high spatial correlation among pixels, but each pixel is better described by its high dimensional spectral feature vector which provides more information when characterizing the similarities among every pair of pixels. Also, to facilitate the fact that boundary usually resides in low density regions in spectral domain, a local density adaptive affinity matrix is presented in this paper. Results will be shown for airborne hyperspectral imagery collected with the HyMAP, AVIRIS, HYDICE sensors.