Unsupervised Change Detection in Remote Sensing Images using Multi-View Learning

The geospatial objects laying on the earth surfaces are changing rapidly due to various reasons. The multi-temporal remote sensing images are widely used data sources to capture changed occurred at a particular location. Change detection is the process to determine disagreement in the nature of geospatial objects of the same location from different time's instances. Unsupervised change detection is an approach to determine the changed and unchanged state of a location without using any label information. The remote sensing images are captured in multiple spectral bands and the extraction of spatial, texture and shape features in each band increases the dimensionality of the data sets. The conventional change detection techniques may perform poor for the high dimensional remote sensing data sets. In this work, a multi-view learning based unsupervised technique has been developed for the unsupervised change detection in temporal remote sensing images. The experimental results on the two benchmark data sets show that the proposed unsupervised multi-view approach has performed better than single view unsupervised change detection techniques.