Abstract
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.
Highlights
Satellite images provide invaluable information on post-disaster conditions to emergency management agencies
Images collected by active (RADAR) and passive sensors (Figure 1) on-board satellites are used for mapping and monitoring the extent of floods and changes over time
Output image generated with a window size of 128 x 128 pixels had the lowest Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) score
Summary
Satellite images provide invaluable information on post-disaster conditions to emergency management agencies. Images collected by remote sensors have been effective for evaluating post-disaster conditions (Brivio et al, 2002). Floods are one of the major disasters that impact natural- and built-ecosystems worldwide. Images collected by active (RADAR) and passive (optical) sensors (Figure 1) on-board satellites are used for mapping and monitoring the extent of floods and changes over time. Optical sensors are limited in terms of collecting data when thick clouds are present. RADAR signals can penetrate cloud cover and collect data on the aerial extent of floods. There are relatively more optical sensors than those that collect RADAR data. More optical images are available for monitoring post-flood conditions
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