Over the last two decades, multiple classifier system (MCS) or classifier ensemble has shown great potential to improve the accuracy and reliability of remote sensing image classification. Although there are lots of literatures covering the MCS approaches, there is a lack of a comprehensive literature review which presents an overall architecture of the basic principles and trends behind the design of remote sensing classifier ensemble. Therefore, in order to give a reference point for MCS approaches, this paper attempts to explicitly review the remote sensing implementations of MCS and proposes some modified approaches. The effectiveness of existing and improved algorithms are analyzed and evaluated by multi-source remotely sensed images, including high spatial resolution image (QuickBird), hyperspectral image (OMISII) and multi-spectral image (Landsat ETM+).Experimental results demonstrate that MCS can effectively improve the accuracy and stability of remote sensing image classification, and diversity measures play an active role for the combination of multiple classifiers. Furthermore, this survey provides a roadmap to guide future research, algorithm enhancement and facilitate knowledge accumulation of MCS in remote sensing community.

As a result of urbanization, land use/land cover classes in urban areas are changing rapidly, and this trend increased in the recent years. Change information detected from multi-temporal remote sensing images can thus help to understand urban development and to effectively support urban planning. Differences in reflectance spectra, easily obtained by multi-temporal remote sensing images, are important indicators to characterize these changes. Although many algorithms were proposed to generate difference images, the results are usually greatly inconsistent. In order to integrate the merits of different algorithms to recognize spectral changes, fusion techniques merging multiple difference images are proposed and implemented in this paper. Feature and decision level fusion are used to combine simple change detectors, and to build an automatic change detection procedure. The proposed approach is tested with multi-temporal CBERS and HJ-1 images, and experimental results demonstrate its effectiveness and reliability. By integrating different change information, the appropriate fusion method can be selected according to the specific application in order to minimize the omission or the commission errors.