The application of threshold methods for image segmentation in oasis vegetation extraction

In this study, three threshold algorithms for image segmentation were used to quickly and reliably determine the threshold of oasis vegetation of Jinta in the arid inland river of Northwest China. And then the extraction results were contrasted. This is a beneficial exploration of automatically extracting the boundary of the oasis. During the experiment, firstly, we obtained the greenness images after tasseled cap transformation of Landsat TM remote sensing images, and then we segmented them with thresholds that were obtained from the three methods: one is Otsu method, which is on basis of global binary image algorithm. In this method, we calculated the between-class variance and within-class variance values of the whole images and took the gray values of the images as the optimal thresholds when the between-class variance reached maximum value. Another is iterative method based on the idea of approximation. The average of the mean gray value of the background and foreground is considered as the threshold. The third is edge detection based method on basis of local binary image algorithm. In this method, we firstly detected and tracked the image edges of the greenness images, and then computed the segmentation threshold by weighting the average pixels values of image edges. These algorithms and processes were carried out by programming in MATLAB. The results show that, the thresholds determined by Otsu and iterative method are a little higher, which results in loss of information within the oasis, while the edge-based detection threshold segmentation on basis of Robert operator is more suitable for the whole image, for it takes into account the internal regularity of the oasis. But on the whole, the outer boundary of oasis vegetation can extracted at full and the threshold can be determined automatically by these three methods, allowing it to be further applied in the extraction of remote sensing information.