AUTOMATIC SHADOW DETECTION FOR HIGH-RESOLUTION REMOTE SENSING DATA

Abstract. Shadow can be casted by daylight or any other light sources. We will not get a clear and quality image if it's hovered by the shadow. Shadows are frequently formed in high-resolution satellite imagery by the limitations of the imaging environment and the presence of high rise structures, and this scenario is true especially in metropolitan regions. Shadow is one of the noteworthy evils in remotely sensed imagery which hinders the precision of information extraction and change identification. To attenuate the effects of shadow in high resolution imagery regarding their supplemental functions, our paper suggests a novel algorithm for shadow masking built on computational methods. Firstly we transformed the images from RGB space to CIELCh space model, next we evaluated a modified Specthem ratio, and then used multilevel thresholding. We also created shadow masks for areas having vegetation, water, and soil. Shadow mask noise was decreased by morphological techniques. The ratio of lighting for the shadowed and unshadowed areas is utilized to create shadow masks, which are then used to remove shadows from the source photos. The thresholding approach creates an initial shadow mask during the shadow detection step, and the morphological filtering method is used to remove the noise and incorrect shadow regions. We also vectorized the raster data which can be further applied for various other studies.