Urban landscape change analysis using satellite imagery and support vector machines

We used a change detection approach based on support vector machine (SVM) to analyze two remotely sensed images in order to analyze urban landscape change on high-dense urban use (HDU), medium-dense urban use (MDU) and low-dense urban use (LDU) in Kunming, China. These two images were subset of a TM image acquired on 16 August 1992 and an ETM+ image acquired on 2 November 2000, respectively. First, we used SVM to classify each subset into HDU, MDU, and LDU. Then, we compared the label values of classified data pixel by pixel to analyze urban landscape changes. In order to obtain high quality training data under the circumstance that existing classification products of sampling area were not available, we proposed a second sampling method to assure obtaining satisfactory training data. The kernel function of SVM was radial basis function (RBF). Optimal model with the best penalty parameter C and the kernel parameter gamma was obtained through training samples. We tested the approach in three sites: northern Kunming, southern Kunming and entire Kunming. Results indicate that the overall urban use has substantially increased during 1992- 2000, while the substantial growth in high-density urban use was achieved at the cost of low-density urban use and partially medium- density urban use.