Time series model based region growing method for image segmentation in remote sensing images

Time series models have been very useful in describing the texture and contextual information of an image. In remote sensing image segmentation, different pattern classes (segments) may differ significantly in texture and thus time series models can be useful for image segmentation of remote sensing images. In this paper, our earlier work of using ARMA model based region growing method for extracting lake region in a remote sensing image (Chen and Ho, 2003) is extended to a general image segmentation procedure using time series based region growing for remote sensing images with some algorithm changes in improving the texture classification results as well as computer calculation efficiency. A first-order autoregressive image model and a second-order autoregressive moving-average image model are implemented for comparison. One advantage of region growing is that only a small number of seed pixels, based perhaps on reliable ground truth, are needed to represent each pattern class. This is different from typical supervised classification which requires a large number of training samples. As the regions grow some pixels near the border of two adjacent segments may be assigned to two (or more) different segments. A statistical hypothesis testing is performed on such pixels so that each pixel will be given a unique assignment. The procedure is applied to the LANDSAT 5 data base in the area of Italy´s MULARGIAS lake region with encouraging preliminary results in segmenting bodies of water and vegetation. In addition, we have tested with good results on USC natural scene data, such as grass, brick, brickwall, plasticbubble, sand... etc