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.
Motion-based video representation for scene change detection
