Multi-scale Segmentation of Remote Sensing Image Based on Watershed Transformation

Image segmentation is an important step for classification and feature extraction of high resolution remote sensing image. The purpose of this study is to find an improved segmentation method suitable for high resolution remote sensing image. Firstly a region homogeneity indictor called H index was introduced. Then the optimized edge gradient was obtained based on the integration of Canny operator and H index. A watershed transformation followed up to acquire the initial segmentation of the remote sensing image. To eliminate the over-segmentation, a multi-scale merging according to object-oriented principle was finally conducted. A multi-spectrum QuickBird remote sensing image was segmented per the above-mentioned method. The improved H gradient image effectively overcame the limitations of week edges in high resolution remote sensing image, and on the whole the QuickBird image was segmented into homogeneity objects. It proves that the improved segmentation method is suitable to high resolution remote sensing images.