Change detection is an important research direction in the field of remote sensing technology. However, for hyperspectral images, the nonlinear relationship between the two temporal images will increase the difficulty of judging whether the pixel is changed or not. To solve this problem, a hyperspectral change detection method is proposed in which the transformation matrices are obtained by using the constraint formula based on the minimum spectral angle, which uses both spectral and spatial information. Further, a kernel function is used to handle the nonlinear points. There are three main steps in the proposed method: first, the two temporal hyperspectral images are transformed into new dimensional space by a nonlinear function; second, in the dimension of observation, all the observations are combined into a vector, and then the two transformation matrices are obtained by using the formula of spectral angle constraint; and third, each pixel is given weight with a spatial weight map, which combined the spectral information and spatial information. Study results on three data sets indicate that the proposed method performs better than most unsupervised methods.
Remote sensing image enhancement algorithm, which can enhance the contrast of image without changing its information, is crucial for the acquisition and analysis of remote sensing information. In this paper, we compare the performance of several state-of-the-art enhancement algorithms in processing remote sensing images. We classify and refine the main idea of each algorithm, and evaluate the enhancement effect with subjective evaluation and objective metrics such as entropy, brightness, contrast and mean gradient. In order to avoid color-enhanced interference, this paper took experiments both on RGB pictures and grayscale pictures. The significance and compared results were shown in the following paper.