Multispectral Quickbird-2 Image Segmentation Based on Vector Field Model

Image segmentation is a valuable approach that performs an object-based rather than a pixel-based analysis of high-spatial resolution satellite image. A multiscale approach for segmenting the pan-sharpened multispectral QuickBird-2 image based on vector field model is proposed. The edge features are obtained using the first fundamental form of the multispectral bands. The response of log Gabor bank filtering of each band is fused as multiscale texture features based on first fundamental form. Then, the image segmentation is implemented based on texture-marked watershed transform. The segmentation accuracy is assessed using discrepancy measures between a reference map and the segmentation. The experimental results show that the proposed approach gives a better solution of integrating spectral and texture information for the segmentation of multispectral remotely sensed image.