Publication Type : Conference Paper
Publisher : The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Source : The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p.45-50 (2020)
Url : https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIV-M-2-2020/45/2020/
Keywords : Disaster response, Enhancement algorithms, floods, Image contrast, Satellite images
Campus : Coimbatore
Center : Computational Engineering and Networking
Year : 2020
Abstract : Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images.
Cite this Research Publication : M. Harichandana, Sowmya V., Sajith Variyar V. V., and Ramesh Sivanpillai, “Comparison of Image Enhancement Techniques for Rapid Processing of POST Flood Images”, in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, pp. 45-50.