[Detecting land use change using PCA-enhancement and multi-source classifier from SPOT images]

Concomitant with the rapid global urbanization process, land use change detection has been the focus and "hot spot" of global change research all the time. In the present study, the rigorous orthorectification was first applied to the SPOT-5 data to guarantee precise geometric correction and image registration. Afterwards, a methodology integrating PCA-enhancement and multi-source classifier was adopted to detect the land use changes in urban area. The results show that the first three PCs derived from multi-temporal-PCA contain most of the spectral information among which unchanged land use is highlighted in PC1 and PC2, and changed land use is mainly enhanced in PC3. The following multi-source classifier integrating unsupervised classifier (ISODATA) and supervised classifier (Maximum Likelihood) accurately extracts all the information. The findings from accuracy assessment demonstrate that the overall accuracy for the proposed method reaches 92.58, KAPPA coefficient is 0.92, and proving figures are also produced for the user's and producer's accuracies. It was further found that the proposed method yielded better accuracy than that of traditional post-classification comparison approach.