ABSTRACT

Remote sensing is emerging as a strong tool to extract information about the earth resources from the satellite imagery. But this information gets affected by shadow in urban areas. High-resolution satellite imagery offer great possibilities for urban mapping. Unfortunately, shadows cast by buildings in high-density urban envi ronments obscure much of the information in the image leading to potentially corrupted classification results or blunders in interpretation. The principle problem caused by the shadows is either a reduction or total loss information in an image. Reduction of information could potentially lead to the corruption of biophysical parameters derived from pixels values, such as vegetation indices. Total loss of information means that areas of the image cannot be interpreted, and value added products, such as digital terrain models, cannot be created. Keeping this in mind an attempt has been made in this project to study the effect of shadow and its impacts on high-resolution imagery of urban area. Panchromatic image of IKONOS satellite sensor is taken for analysis and its statistical parameters are studied. The image is segmented based on property based and image based techniques according to the spectral and geometric properties of images for detecting shadow regions. This segmentation detection technique is Region growing segmentation which misleads water contained image that is water pixel also identified as shadow pixel. This misleading is corrected with region filtering technique based on the simple logic that water body region have low variance than the shadow region. And from these detected shadow regions, different radiometric enhancement techniques (histogram matching, lightness recovery, gamma correction) are applied to enhance the information under shadow regions and results are discussed.