An Improved Independent Component Analysis Method for Ground Targets Information Identification

The remote sensing technology can extract change information of land cover quickly, and is widely used in real-time monitoring land use changing and urban expansion. According to the characteristics of land cover information identification present in high resolution remote sensing images, a new identification algorithm of land cover change information based on independent component analysis (ICA) is proposed, which includes ICA processing, feature space optimization, and probability learning technologies. In the experiments, the land cover change information was extracted quickly using the two Google Earth images of different periods, and the extraction accuracy were evaluated. The results show that: firstly, this propose algorithm is proved to be precise, fast and easy to operate; secondly, the rate of changing to the origin areas in buildings, green fields, and roads are 98.38%, 96.57%, and 74.55%, respectively; thirdly, the urban land cover change information identification has a good image quality and it is suitable for visual and rapid mapping.