Remote sensing provides a new idea and an advanced method for lithology identification, but lithology identification by remote sensing is quite difficult because 1. the disciplines of lithology identification in a concrete region are often quite different from the experts' experience; 2. In the regions with flourishing vegetation, lithology information is poor, so it is very difficult to identify the lithologies by remote sensing images...An intelligent method proposed in this paper for lithology identification based on support vector machine (SVM) and adaptive cellular automata (ACA) is expected to solve the above problems. The method adopted Landsat-7 ETM+ images and 1:50000 geological map as the data origins. It first derived the lithology identification factors on three aspects: 1. spectra, 2. texture and 3. Vegetation cover. Second, it plied the remote sensing images with the geological map and established the SVM to obtain the transition rules according to the factor values of the samples. Finally, it established an ACA model to intelligently identify the lithologies according to the transition and neighborhood rules. In this paper an ACA model is proposed and compared with the traditional one.
In this article, we proposed an effective adaptive 2-dimension blind watermarking algorithm based on feature of a remote sensing image. This algorithm exploited a gray image as the watermark, pretreated the watermark image by Arnold confusion and wavelet compression, and embedded it into the selected subband of wavelet transformation domain of the remote sensing image according to neighboring symbol's mean value and odd-even adjugement rule, moreover, detected watermarks without the original remote sensing image. The attack analysis and experimental results show that the watermarking algorithm is transparent and robust, with accurate watermark detecting results and low complexity, and it also has strong robustness against various image attacks such as JPEG lossy compression, median filtering, additive noise, scaling, cropping, rotation, random geometrical attack and Stirmark attack. Furthermore, after embedding watermarks, there is almost no influence on such applications of the remote sensing image as edge detection and image classification.
In order to hide secrete information in remote sensing image, we proposed an algorithm for secrete information hiding which was adaptive to the feature of remote sensing image. Firstly, we segmented and extracted the secrete information in remote sensing image, and made supplement of gray values in the area corresponding with the secrete information and then produced the disguised remote sensing image which was wiped off secrete information. Then we used for reference the idea of digital watermarks and feature of HVS (Human Visual System) and embedded the secrete sub-image imperceptibly and adaptively into the disguised remote sensing image to produce the disguised remote sensing image in which there hid secrete sub-image. In addition, during the course of extracting secrete information and resuming the remote sensing image, this algorithm didn’t need the original remote sensing image and was a blind one. To those algorithms for information hiding, imperceptivity and amount of hidden information are the most important and robustness is less. And experimental results show that this algorithm is not only quite transparent and has a good effect for large amount of secrete information hiding, but also has a strong robustness against such image attacks as JPEG lossy compression, median filtering, noise adding, scaling, cropping and rotation. Furthermore this algorithm has no influence on such applications as edge detection and image classification of the disguised remote sensing image which has been hidden the secrete information.