Remote sensing image segmentation is an important problem in the processing of remote sensing images. Existing methods for remote sensing image segmentation include supervised, weakly supervised and unsupervised approaches. Supervised and weakly supervised approaches require certain prior statistical knowledge on different regions in remote sensing images, while unsupervised approaches are able to accomplish the task of segmentation to a certain extent in the absence of such knowledge. The purpose of this paper is to realize an unsupervised image segmentation method that can be applied to remote sensing images. The approach utilizes a hidden Markov model to accurately describe the statistical distributions of the R, G and B components of pixels and the correlations among those of different pixels. The labels in a segmentation result are described by the states in the hidden Markov model and the segmentation with the maximum likelihood is obtained with a dynamic programming approach based on the Viterbi’s algorithm. Experimental results prove the feasibility of the proposed approach for segmentation. A comparison with state-of-the-art segmentation methods show that the proposed approach can lead to segmentation results with improved accuracy. The proposed approach is thus potentially useful for improving the accuracy of remote sensing applications that require segmentations of remote sensing images.
In the past decade, a large amount of important digital data has been created and stored in the form of color images; the protection of such data from undesirable accesses has become an important problem in information security. In this paper, a new approach based on an evolutionary framework is proposed for the secure encryption of color images. The image contents in a color image are first fully scrambled with a sequence of bit-level operations determined by a number of integer keys. A scrambled image is then encrypted with keys generated from an evolutionary process controlled by a set of chaotic systems. Analysis and experiments show that the proposed approach can generate encrypted color images with high security. In addition, the performance of the proposed approach is compared with that of a few state-of-the-art approaches for color image encryption. The results of the comparison suggest that the proposed approach outperforms the other approaches in the overall security of encrypted images. The proposed approach is thus potentially useful for applications that require color image encryption.
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