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Shadow features are taken into consideration during image segmentation, and then, according to the statistical features of the images, suspected shadows are extracted. Furthermore, some dark objects which could be mistaken for shadows are ruled out according to object properties and spatial relationship between objects. For shadow removal, inner–outer outline profile line (IOOPL) matching is used. First, the IOOPLs are obtained with respect to the boundary lines of shadows. Shadow removal is then performed according to the homogeneous sections attained through IOOPL similarity matching.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:            The model based methods is often used in some specific scene conditions such as aerial image analysis and video monitoring.	PROPOSED CONCEPT:            The shadow features are evaluated through image segmentation, and suspected shadows are detected with the threshold method.            Object properties such as spectral features and geometric features are combined with a spatial relationship in which the false shadows are ruled out (i.e., water region).
EXISTING ALGORITHM:            Model-Based Method	PROPOSED TECHNIQUE:            Object-Oriented Shadow Detection and Removal Method
ALGORITHM DEFINITION:            It uses prior information such as scene, moving targets, and camera altitude to construct shadow models.	TECHNIQUE DEFINITION:            First, the shadow features are evaluated through image segmentation, and suspected shadows are detected with the threshold method.            Object properties such as spectral features and geometric features are combined with a spatial relationship in which the false shadows are ruled out.
DRAWBACKS:            Shadows may cause incorrect results during change detection.            It is not suitable for high resolution images.	ADVANTAGES:            The proposed stably and accurately identify shadows.            It is suitable for high-resolution urban remote sensing images.