Scene-Change Detection Based on Multi-Feature-Fusion Latent Dirichlet Allocation Model for High-Spatial-Resolution Remote Sensing Imagery

With the continuous development of high-spatial-resolution ground observation technology, it is now becoming possible to obtain more and more high-resolution images, which provide us with the possibility to understand remote sensing images at the semantic level. Compared with traditional pixel- and object-oriented methods of change detection, scene-change detection can provide us with land use change information at the semantic level, and can thus provide reliable information for urban land use change detection, urban planning, and government management. Most of the current scene-change detection methods are based on the visual-words expression of the bag-of-visual-words model and the single-feature-based latent Dirichlet allocation model. In this article, a scene-change detection method for high-spatial-resolution imagery is proposed based on a multi-feature-fusion latent Dirich- let allocation model. This method combines the spectral, textural, and spatial features of the high-spatial-resolution images, and the final scene expression is realized through the topic features extracted from the more abstract latent Dirichlet allocation model. Post-classification comparison is then used to detect changes in the scene images at different times. A series of experiments demonstrates that, compared with the traditional bag-of-words and topic models, the proposed method can obtain superior scene-change detection results.

Fuzzy Rule Based Video Watermarking in DWT-SVD Domain

This paper addresses the issues in video copyright using DWT and SVD. The prevailing algorithms countermeasure various attacks and they do not contemplate on the redundancy of frames in the video. Proposed methodology focuses on the identification of non-redundant frames by introducing a fuzzy model for reducing the processing time. The frequently changed scenes are identified by scene change detection algorithm. The key frames are effectively identified from each scene by fuzzy rules using entropy, absolute mean difference and absolute difference of frame variance of the video frames. DWT is applied to the key frames. The watermark image is divided into number of blocks based on the number of key frames selected in the scene. The order of embedding the watermark block in each scene is different. The SVD is applied to the key frames and watermark. In the embedding process, the singular values of key frame are added to the Principal Component (PC) of the watermark bock. The experimental results show that the proposed methodology is resilient to image processing, frame based attacks and also resolves the false positive problem as well as improves the robustness and imperceptibility of video and watermark.