Influence of Noise Reduction on Hyperspectral Image Change Detection Accuracy
Meanwhile, in the field of hyperspectral remote sensing, change detection becomes a day to day area of interest. As change detection refers to the process of identifying interesting changes that occur to a spatial area over which imagery has been collected on multiple and different times. The acquired HSI images are mostly corrupted by noise. This work studies a performance analysis of change detection with and without noise reduction method for hyper spectral images. The methods in this study include, General Analysis Prior (GAP) Algorithm as noise reduction method and change vector analysis (CVA) as the change detection method by implementing MATLAB software as a main tool.
Keywords - Change detection, Change vector analysis (CVA), General Analysis Prior (GAP), Hyperspectral imaging (HSI), Noise reduction.