Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban landcover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition evidences. The proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban areas. The effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods (i.e. change vector analysis and principal component analysis-based method). In particular, the proposed sub-pixel change detection approach not only provides the binary change information, but also obtains the characterization about change direction and intensity, which greatly extends the semantic meaning of the detected change targets.