Unsupervised approach for change map generation

Change detection is the process of automatically identifying and analyzing region that have undergone spatial or spectral changes from multi temporal images. Detecting and representing change provides valuable information of the possible transformations a given scene has suffered over time. Change detection is used in several applications (eg. Disaster management, deforestation, urbanization, etc). In the proposed unsupervised method co-registered and radiometrically corrected temporal images are used as input. Using this, absolute valued image and log ratio image is calculated to get difference image. These difference images are fused using Discrete Wavelet Transform (DWT). Then, min-mean normalization is applied to the get filtered data. The normalized data is clustered into two groups using K-means clustering algorithm as changed pixels and unchanged pixels. Experiment result is also calculated using two different ways. In first, fused image data is given to Principal Component Analysis (PCA) and clustering is done using K-means algorithm and in second way Fuzzy c-means clustering algorithm is used to cluster image data.