An Unsupervised Urban Change Detection Procedure by Using Luminance and Saturation for Multispectral Remotely Sensed Images

Unsupervised change detection techniques have been widely employed in the remote-sensing area when suitable reference data is not available. Image (or Index) differencing is one of the most commonly used methods due to its simplicity. However, past applications of image differencing were often inefficient in separating real change and noise due to the lack of steps for feature selection and integration of contextual information. To address these issues, we propose a novel unsupervised procedure which uses two complementary features, namely luminance and saturation, extracted from multispectral images, and combines T-point thresholding, Bayes fusion, and Markov Random Fields. Through a case study, the performance of our proposed procedure was compared with other three unsupervised change-detection methods including Principle Component Analysis (PCA), Fuzzy c-means (FCM), and Expectation Maximum-Markov Random Field (EM-MRF). The change detection results from our proposed method are more compact with less noise than those from other methods over urban areas. The quantitative accuracy assessment indicates that the overall accuracy and Kappa statistic of our proposed procedure are 95.1 percent and 83.3 percent, respectively, which are significantly higher than the other three unsupervised change detection methods.