Shadow Detection From Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

Abstract

ABSTRACT:Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-theart shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of the ratio algorithm. The differences in the characteristics of the two satellite imageries in terms of spatial and spectral resolution can play an important role in the estimation and detection of the shadow of urban objects.

A Shadow-Overlapping Algorithm for Estimating Building Heights From VHR Satellite Images

“…The first task in shadow-based H B estimation is the extraction of shadow regions from VHR satellite images. In this respect, [6] reported two widely used techniques: ratio-band and Graph-Cut partitioning via kernel mapping, with overall accuracies of 85% and 79%, based on two performance metrics, F 1 score (harmonic mean of precision and recall) and probabilistic Rand index, respectively. A semiautomatic approach was proposed in [7] to estimate H B from a single satellite image by manually adjusting the height of a simulated building and then matching the projected shadow with the actual.…”

“…To reliably extract the actual shadows of buildings, S Ac , we applied a refined version of our previously developed shadow detection algorithm, A S [6], as proposed by Rüfenacht et al [16]. Candidate shadow regions are obtained by applying a nonlinear mapping function to the extracted dark pixels from both the visible, V , and near infrared, NIR, bands using…”

“…We, therefore, proposed a morphological filter to remove all small objects, i.e., the nonshadow pixels. To keep only the areas of building shadows, the process of elimination of small objects is controlled by removing the areas <100 pixels [6]. In this letter, A S is improved further to detect and eliminate the vegetation cover by applying normalized difference vegetation index.…”

“…where M Bc is the complement of the building object equation (6), and O 3×3 is a matrix of ones. ⊗ and ∩ denote morphological dilation and pixel value intersection, respectively, using…”

A Novel MRI Image Segmentation Algorithm based on Modified Neural Network Model

“…How to improve the accuracy of medical image segmentation is a hot spot in the field of medical image processing [9][10]. Based on the literature survey, the classic image segmentation algorithms can be summarized as the following parts.…”