2-D Time Series Model Based Support Vector Machine for Remote Sensing Image Segmentation

A method of modeling an image based on 2-D Time Series that merges with the popular MultiClass Support Vector Machine (SVM) as a generalized linear classifier is proposed. In this paper, we present the classification results on both remote sensing and texture type images. Both Binary SVM Classifier and Multiclass SVM are implemented and discussed. The 2-D Time Series models have been useful in describing the texture and contextual information of an image. The different pattern of classes (segments) may differ significantly in texture and thus time series models can be useful for image segmentation of the images. The image model in our earlier work of using ARMA model based region growing method for extracting lake region in a remote sensing image[l] and the extension to a general image segmentation procedure using time series based region growing for remote sensing images[2] has been extended to using SVM classifier instead of region growing method. The procedure is applied to a city near lake area as shown in figure 2 with encouraging preliminary results in segmenting bodies of water and city building. Also, the USC (U. Southern California) texture images (brick, grass pigtail ...etc) are used in this new SVM classifier method.