A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images

ObjectiveReconstructing VHR images by removing the

shadows.

Abstract

In order to make the image quality we should remove the shadows from the images. The presence of shadows in very high resolution (VHR) images can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation. The detection and classification tasks are implemented by means of the state-of-the-art support vector machine approach.

Existing SystemThe presence of shadows in very high resolution (VHR) images can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation.

Disadvantage

High spatial resolution entails also some drawbacks like the unsought presence of shadows, particularly in urban areas where there are larger changes in surface elevation and consequently longer shadows.

No Image Enhancement.

Proposed SystemIn this proposed approach, an alternative method is proposed to solve both problems of detection and reconstruction of shadow areas. Shadow detection is performed through a hierarchical supervised classification scheme, while the proposed reconstruction relies on a linear correlation function, which exploits the information returned by the classification. The whole processing chain includes also two important capabilities: 1) a rejection mechanism to limit as much as possible reconstruction errors and 2) explicit handling of the shadow borders. 1) Gamma correction; 2) histogram matching; and 3) linear correlation [16]. In [2], the authors assume that the surface texture does not radically change when it is shaded. Accordingly, to remove shadows, they perform a contextual texture analysis between a segment of shadow and its neighbours. Knowing the kind of surface under the shadow, a local gamma transformation is then used to restore the shadow area.

Advantage

A quality check mechanism is integrated in order to reduce subsequent misreconstruction problems.

Image Enhancement is happening.

Modules> Read and write image We have to write imread and imwrite functions to read an image from the user. The user need an input image from the user and in a variable assigned. The user can give the image name as well as browse and select also.

> Apply morphological image processing

We are applying morphological operations on the image. Basically clear the noise from the image. These process will come under post processing and The

transition in between shadow and non-shadow areas can raise problems such as boundary ambiguity, color inconstancy, and illumination variation can be calculated. Also in difficult places we are calculating the light sources. Then border reconstruction is done due to the shadow values. This we are doing repeatedly.

> Remove ShadowDue to the Shadow we are diving into two types of

shadows in the 3D images. In this we are deleting the values of the cast shadow. The self shadow is part of the image. We while we are removing the shadow we should know the shadow type.> Image Reconstruction

After removing the cast shadow we have to get back the original image. In this we after removing the cast shadow, we have to construct the image back from the matrix value. that will be done by applying adaptive morphological filters