Automatic and Accurate Shadow Detection Using Near-Infrared Information

Rüfenacht, Dominic; Fredembach, Clément; Süsstrunk, Sabine

doi:10.1109/tpami.2013.229

Cited by 81 publications

(35 citation statements)

References 33 publications

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“…We have selected a simple condition for detecting shadows in this experiment, which is a cast shadow of a detached building within an urban area. The first approach was accomplished by Rufenacht et al (2014). The approach is to compute ratios between the nearinfrared (NIR) and visible bands on a pixel-by-pixel basis, which allows disambiguation between shadows and dark objects.…”

Section: Shadow Detection Using Near-infrared Informationmentioning

confidence: 99%

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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: Shadow Detection Using Near-infrared Informationmentioning

confidence: 99%

“…The whole approach and all equations are already welldescribed in Rufenacht et al (2014). The multi-image approach was applied on the Quickbird satellite imagery (visible and INR bands) with spatial resolution 60 cm, the study area being in Ankara, Turkey.…”

Section: Shadow Detection Using Near-infrared Informationmentioning

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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“…One type is based on interaction, called semiautomatic methods [5]. Interaction-based methods can achieve good performance results where user-supplied information should be provided.…”

Section: Introductionmentioning

confidence: 99%

Joint Model and Observation Cues for Single-Image Shadow Detection

2016

Remote Sensing

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Shadows, which are cast by clouds, trees, and buildings, degrade the accuracy of many tasks in remote sensing, such as image classification, change detection, object recognition, etc. In this paper, we address the problem of shadow detection for complex scenes. Unlike traditional methods which only use pixel information, our method joins model and observation cues. Firstly, we improve the bright channel prior (BCP) to model and extract the occlusion map in an image. Then, we combine the model-based result with observation cues (i.e., pixel values, luminance, and chromaticity properties) to refine the shadow mask. Our method is suitable for both natural images and satellite images. We evaluate the proposed approach from both qualitative and quantitative aspects on four datasets. The results demonstrate the power of our method. It shows that the proposed method can achieve almost 85% F-measure accuracy both on natural images and remote sensing images, which is much better than the compared state-of-the-art methods.

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“…1-(b) shows a pair of color The growing interest in joint capture of color and NIR images on a single sensor [1][2][3][4] is caused by recent developments in using these images in computational photography and computer vision. Combining color and NIR has proven useful in low light imaging [5], scene recognition [6], shadow detection [7], etc. These applications are enabled as siliconsensors, used in most digital cameras, are sensitive to both visible and NIR radiations.…”

Section: Introductionmentioning

confidence: 99%

Multiscale guided deblurring: Chromatic aberration correction in color and near-infrared imaging

Sadeghipoor

Méndez³

et al. 2015

2015 23rd European Signal Processing Conference (EUSIPCO)

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Chromatic aberration, caused by photographic lens imperfections, results in the image of only one spectral channel being sharp, while the other channels are blurred depending on their wavelengths difference with the sharp channel.We study chromatic aberration for a system that jointly records color and near-infrared (NIR) images on a single sensor. Chromatic aberration in such a system leads to a blurred NIR image when the color image is in-focus and sharp. We propose an algorithm that deblurs the NIR image using the gradients of the sharp color image, as both scene representations are generally similar. However, the details of these images often exhibit significant differences due to varying scene reflection and absorption in the corresponding bands. To account for this, we compute the correlation between color and NIR gradients, and use the gradients of the color image in reconstructing NIR only where the gradients are highly correlated. We propose a multiscale scheme that gradually deblurs NIR and accurately computes similarities between color and NIR gradients. Experimental results show that our algorithm recovers details of NIR without producing visible artifacts.

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Automatic and Accurate Shadow Detection Using Near-Infrared Information

Cited by 81 publications

References 33 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

Joint Model and Observation Cues for Single-Image Shadow Detection

Multiscale guided deblurring: Chromatic aberration correction in color and near-infrared imaging

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