Multi-spectral False Color Shadow Detection

Teke, Mustafa; Baseski, Emre; Ok, Ali Özgün; Yüksel, Baríş; Şenaras, Çağlar

doi:10.1007/978-3-642-24393-6_10

Cited by 41 publications

(28 citation statements)

References 11 publications

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“…Because of the morphological filtering in the shadow detection stage, some small shadow areas were not removed and were remained in the classification map after shadow reconstruction. Another different recent work, a new index for detecting shadows regions, was introduced by Teke et al (2011). In this approach the use of near-infrared information in combination with visible information (especially, green and red bands) of VHR satellite images is utilised to generate a false colour image.…”

Section: Cs Dsmentioning

confidence: 99%

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

Kadhim

Mourshed

Bray

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Cs Dsmentioning

confidence: 99%

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

Kadhim

Mourshed

Bray

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…On the other hand, the chromagenic approach is not limited to shadows, and can incorporate a number of multi-illuminant scenes. Closer to our method, Teke et al [29] create a falsecolor image of satellite imagery by replacing the blue channel of the RGB image with NIR. By analyzing the difference between saturation and intensity of the falsecolor image, and subsequent independent processing of vegetation regions, they are able to obtain shadow maps for remote sensing applications.…”

Section: Multi-image Approachesmentioning

confidence: 99%

Automatic and Accurate Shadow Detection Using Near-Infrared Information

Rüfenacht

Fredembach

Süsstrunk

2014

IEEE Trans. Pattern Anal. Mach. Intell.

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Abstract-We present a method to automatically detect shadows in a fast and accurate manner by taking advantage of the inherent sensitivity of digital camera sensors to the near-infrared (NIR) part of the spectrum. Dark objects, which confound many shadow detection algorithms, often have much higher reflectance in the NIR. We can thus build an accurate shadow candidate map based on image pixels that are dark both in the visible and NIR representations. We further refine the shadow map by incorporating ratios of the visible to the NIR image, based on the observation that commonly encountered light sources have very distinct spectra in the NIR band. The results are validated on a new database, which contains visible/NIR images for a large variety of real-world shadow creating illuminant conditions, as well as manually labelled shadow ground truth. Both quantitative and qualitative evaluations show that our method outperforms current state-of-the-art shadow detection algorithms in terms of accuracy and computational efficiency.

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“…1b). A recently proposed index is utilized to detect shadow areas (Teke et al, 2011). The index depends on a ratio computed with the saturation and intensity components of the Hue-Saturation-Intensity (HSI) space, and the basis of the HSI space is a false colour composite image (NIR, R, G).…”

Section: Detection Of Vegetation and Shadow Areasmentioning

confidence: 99%

Automated Extraction of Buildings and Roads in a Graph Partitioning Framework

2013

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents an original unsupervised framework to identify regions belonging to buildings and roads from monocular very high resolution (VHR) satellite images. The proposed framework consists of three main stages. In the first stage, we extract information only related to building regions using shadow evidence and probabilistic fuzzy landscapes. Firstly, the shadow areas cast by building objects are detected and the directional spatial relationship between buildings and their shadows is modelled with the knowledge of illumination direction. Thereafter, each shadow region is handled separately and initial building regions are identified by iterative graph-cuts designed in a two-label partitioning. The second stage of the framework automatically classifies the image into four classes: building, shadow, vegetation, and others. In this step, the previously labelled building regions as well as the shadow and vegetation areas are involved in a four-label graph optimization performed in the entire image domain to achieve the unsupervised classification result. The final stage aims to extend this classification to five classes in which the class road is involved. For that purpose, we extract the regions that might belong to road segments and utilize that information in a final graph optimization. This final stage eventually characterizes the regions belonging to buildings and roads. Experiments performed on seven test images selected from GeoEye-1 VHR datasets show that the presented approach has ability to extract the regions belonging to buildings and roads in a single graph theory framework.

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Multi-spectral False Color Shadow Detection

Cited by 41 publications

References 11 publications

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

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

Automatic and Accurate Shadow Detection Using Near-Infrared Information

Automated Extraction of Buildings and Roads in a Graph Partitioning Framework

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