Shadow Detection from VHR Images using Clustering and Classification

This project mainly focus to get the high resolution color remote sensing image, and also undertaken to remove the shaded region in the both urban and rural area. Some of the existing projects are involved to detect the shaded region and then eliminate that region, but it has some drawbacks. The detection of the edges will be affected mostly by the application of the external parameters. The edge detection process can be more helpful in the detection of the objects so that the objects can be used for further processing. In this process we have implement the Scale Space algorithm is used to detect the shadow region and extract the feature from the shadow region. Scale Space is simplest in region-base image segmentation methods. The concept of Scale Space algorithm is check the neighboring pixels of the initial seed points. Then determine whether those neighboring pixels are added to the seed points or not. In the Scale Space threshold algorithm Pixels are placed in the region based on their properties or the properties of the nearby pixel values. Then the pixel containing the similar properties is grouped together and then the large numbers of pixels are distributed throughout the image

Scale Space Based Object-Oriented Shadow Detection and Removal from Urban High-Resolution Remote Sensing Images

This task mostly center to get the high resolution color remote sensing image, and furthermore attempted to eliminate the concealed district in the both metropolitan and country region. A portion of the current activities are included to recognize the concealed district and afterward dispense with that area, yet it has a few disadvantages. The discovery of the edges will be influenced generally by the utilization of the outside boundaries. The edge location cycle can be more useful in the recognition of the articles with the goal that the items can be utilized for additional handling. In this cycle we have execute the Scale Space algorithm is utilized to identify the shadow area and concentrate the component from the shadow district. Scale Space is least complex in area base image segmentation strategies. The idea of Scale Space algorithm is check the neighboring pixels of the underlying seed focuses. At that point decide if those neighboring pixels are added to the seed focuses or not. In the Scale Space limit algorithm Pixels are set in the area dependent on their properties or the properties of the close by pixel esteems. At that point the pixel containing the comparable properties is gathered and afterward the enormous quantities of pixels are circulated all through the image

Shadow Detection and Reconstruction in Satellite Images using Support Vector Machine and Image In-painting

ABSTRACT: In this paper an approach for the detection of shadows in satellite images using Support Vector Machine is proposed. The first step is to classify the shadow and non-shadow regions with the help of Support Vector Machine. In order to remove the noise in the classified image median filter is used. The reconstruction of the shadow areas is done by using image in-painting technique. This technique is used to retain the missing parts in an image due to shadows. The performances are evaluated by means of Peak Signal-to-Noise Ratio (PSNR), and Mean Square Error (MSE)

Adaptive Shadow Detection Using a Blackbody Radiator Model

The application potential of remotely sensed optical imagery is boosted through the increase in spatial resolution, and new analysis, interpretation, classification, and change detection methods are developed. Together with all the advantages, shadows are more present in such images, particularly in urban areas. This may lead to errors during data processing. The task of automatic shadow detection is still a current research topic. Since image acquisition is influenced by many factors such as sensor type, sun elevation and acquisition time, geographical coordinates of the scene, conditions and contents of the atmosphere, etc., the acquired imagery has highly varying intensity and spectral characteristics. The variance of these characteristics often leads to errors, using standard shadow detection methods. Moreover, for some scenes, these methods are inapplicable. In this paper, we present an alternative robust method for shadow detection. The method is based on the physical properties of a blackbody radiator. Instead of static methods, this method adaptively calculates the parameters for a particular scene and allows one to work with many different sensors and images obtained with different illumination conditions. Experimental assessment illustrates significant improvement for shadow detection on typical multispectral sensors in comparison to other shadow detection methods. Examples, as well as quantitative assessment of the results, are presented for Landsat-7 Enhanced Thematic Mapper Plus, IKONOS, World- View-2, and the German Aerospace Center (DLR) 3K Camera airborne system