It is a big challenge to segment remote sensing images especially multispectral satellite imagery due to their unique features. In consideration of the fact that satellite imagery are playing an increasingly important role, we conducted the research on segmentation of such imagery. Since multispectral satellite imagery are more similar to natural color images than to other types of images, it is more likely that studies on natural color images segmentation can be extended to multispectral satellite imagery. The obstacle of applying these studies into multispectral satellite imagery lies into their inefficiency when dealing with the large size of images. Therefore, based on a natural color image segmentation approach -- JSEG, we proposed a more efficient one. First, a grid-based cluster initialization approach is proposed to obtain the initial cluster centers, based on which, a fast image quantization approach is implemented. Second, a feature image named J-image to describe local homogeneity is obtained. Then a watershed approach is applied to the J-image, and initial segmentation results are obtained. At last, based on the histogram similarity of each region, a simplified growth merging approach is proposed and the final segmentation results are obtained. By comparing the result of the JSEG approach and the proposed one, we found that the latter is rather efficient and accurate. Advice on further studies is also presented.
In many cases, segmentation approaches for remotely sensed imagery only deal with grey values, which makes them incompetent for segmenting high resolution imagery because texture features are more clearly displayed on them. On the other hand, texture segmentation approaches utilizing both spectral and texture features are, however, very complicated and time consuming which prevents their application. Therefore, to develop simple and effective segmentation approaches for high resolution satellite imagery is very important. In this article, a simple unsupervised segmentation approach for high resolution satellite imagery is proposed. First, wavelet decomposition is utilized to downsample each band of a multiband image. Then a gradient criterion to incorporate local spectral and texture features is utilized to produce a gradient feature image in which pixels with the high and low values correspond to region boundaries and region interiors respectively. Subsequently, a watershed segmentation approach is implemented based on the gradient feature image. Finally, by taking a strategy to minimize the overall heterogeneity increased within segments at each merging step, an improved merging process is performed. Experiments on Quickbird images show that the proposed method provides good segmentation results on high resolution satellite imagery.