Unsupervised segmentation of hyperspectral images

In this paper, we propose a new unsupervised segmentation method for hyperspectral images using edge fusion. We first remove noisy spectral band images by examining the correlations between the spectral bands. Then, the Canny algorithm is applied to the retained images. This procedure produces a number of edge images. To combine these edge images, we compute an average edge image and then apply a thresholding operation to obtain a binary edge image. By applying dilation and region filling procedures to the binary edge image, we finally obtain a segmented image. Experimental results show that the proposed algorithm produced satisfactory segmentation results without requiring user input.