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Unsupervised-Change-Detection

Change detection is the process of identifying differences in land cover over time. As human and natural forces continue to alter the landscape, it is important to develop monitoring methods to assess and quantify these changes. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, are allowing for efficient identification of change patterns and the prediction of areas of growth.

This repository will be outlining an unsupervised method for change detection. It involves the automatic analysis of the change data, i.e. the difference image, constructed using the multi temporal images. A difference image is the pixel-by-pixel subtraction of the 2 images. Eigen vectors of pixel blocks from the difference image will then be extracted by Principal Component Analysis (PCA). Subsequently, a feature vector is constructed for each pixel in the difference image by projecting that pixel’s neighbourhood onto the Eigen vectors. The feature vector space, which is the collection of the feature vectors for all the pixels, upon clustering by K-means algorithm gives us two clusters – one representing pixels belonging to the changed class, and other representing pixels belonging to the unchanged class. Each pixel will belong to either of the clusters and hence a change map can be generated.

Automatic change detection in images of a region acquired at different times is one the most interesting topics of image processing. Such images are known as multi temporal images. Change detection involves the analysis of two multi temporal satellite images to find any changes that might have occurred between the two time stamps. It is one of the major utilization of remote sensing and finds application in a wide range of tasks like defence inspections, deforestation assessment, land use analysis, disaster assessment and monitoring many other environmental/man-made changes

Implemented from this Paper