A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data

Tolt, Gustav; Shimoni, Michal; Ahlberg, Jörgen

doi:10.1109/igarss.2011.6050213

Cited by 57 publications

(39 citation statements)

References 14 publications

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“…The mixture between shadows and nonshadows in the training examples become a serious problem. While SVM is used in many researches (Guo et al, 2011, Tolt et al, 2011, Lorenzi et al, 2012, SVM aims to find an optimal boundary hyperplane between classes and would put lots of effort on the mislabeled samples, resulting in the over fitting problem. However, if the multivariate models of two classes can be estimated properly from relatively large portions of correctly labeled samples, a better classification can be obtained.…”

Section: Qda Shadow Classificationmentioning

confidence: 99%

“…Still, a large amount of manual work is required. Model-based methods take a 3D model to reconstruct shadow for the image with sun position and azimuth angle by ray tracing (Tolt et al, 2011) or z-buffer (Gorte and van der Sande, 2014). However, the 3D information which is often not very accurate and matching with images results in the poor shadow detection in the image.…”

Section: Introductionmentioning

confidence: 99%

“…However, the 3D information which is often not very accurate and matching with images results in the poor shadow detection in the image. An supervised learning approach (Tolt et al, 2011), a support vector machine (SVM), is applied to improve shadow detection by using reconstructed image to provide free training examples. In order to reduce mislabeled samples, the interior of large shadows or non-shadows in the reconstructed images are chosen for training.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Shadowdetection From VHR Aerial Images in Urban Area by Using 3d City Models and a Decision Fusion Approach

Zhou

Gorte

2017

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

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ABSTRACT:In VHR(very high resolution) aerial images, shadows indicating height information are valuable for validating or detecting changes on an existing 3D city model. In the paper, we propose a novel and full automatic approach for shadow detection from VHR images. Instead of automatic thresholding, the supervised machine learning approach is expected with better performance on shadow detection, but it requires to obtain training samples manually. The shadow image reconstructed from an existing 3D city model can provide free training samples with large variety. However, as the 3D model is often not accuracy, incomplete and outdated, a small portion of training samples are mislabeled. The erosion morphology is provided to remove boundary pixels which have high mislabeling possibility from the reconstructed image. Moreover, the quadratic discriminant analysis (QDA) which is resistant to the mislabeling is chosen. Further, two feature domains, RGB and ratio of the hue over the intensity, are analyzed to have complementary effects on better detecting different objects. Finally, a decision fusion approach is proposed to combine the results wisely from preliminary classifications from two feature domains. The fuzzy membership is a confidence measurement and determines the way of making decision, in the meanwhile the memberships are weighted by an entropy measurements to indicate their certainties. The experimental results on two cities in the Netherlands demonstrate that the proposed approach outperforms the two separate classifiers and two stacked-vector fusion approaches.

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Section: Qda Shadow Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Shadowdetection From VHR Aerial Images in Urban Area by Using 3d City Models and a Decision Fusion Approach

Zhou

Gorte

2017

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

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“…It provides a valuable source of information about the environment which can be applied in many areas of human activity such as change detection in rural landscape [1,2], solar radiation modeling [3][4][5], change detection in urban areas [6,7], urban planning [8], GNSS multipath prediction [9], sound propagation modeling [10], wind flow simulation [11], power-line corridors management [12] or visibility analysis [13]. The crucial issue for the application of the DSM is its reliability.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the Redundancy of Multiple Overlapping Aerial Images for Dense Image Matching Based Digital Surface Model Generation

Dominik

2017

Remote Sensing

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Abstract:In recent years, significant development in the domain of dense image matching (DIM) can be observed. Meanwhile, in most countries, aerial images are acquired countrywide on a regular basis with decreasing time intervals and increasing image overlaps. Therefore, aerial images represent a growing potential for digital surface model (DSM) acquisition and updating. Surface reconstruction by image matching, in most cases, requires dealing with the redundancy caused by multiple overlapping images. Many approaches considering this redundancy in the surface reconstruction process have been developed. However, there is no commonly accepted procedure for this task. From the experience of the author, it can be stated that currently applied methods show some limitations regarding DSM generation from aerial images. Therefore, it is claimed that there is room for the development of new algorithms for integration of dense image matching results from multiple stereo pairs. Methods dedicated to aerial image based DSM generation that would exploit the specificity of this task are desirable. In this paper, an approach to compute the DSM elevations from redundant elevation hypotheses derived by pairwise dense image matching is presented. The proposed approach takes into account the base-to-height (b/h) ratio of stereo pairs, the distribution of elevation hypotheses from multiple stereo pairs and the neighboring elevations. An algorithm of selection of the elevation hypotheses used for the calculation of the final DSM elevation for each grid cell was developed. The algorithm was used to generate the DSM based on two sets of aerial images having significantly different acquisition parameters. The results were compared to the models obtained from several commonly used software packages for image based DSM generation. The quality assessment was carried out by visual inspection of terrain profiles and shaded surface display as well as by the planarity control of flat parts of the terrain. The assessment of the results showed that the application of the proposed algorithm can bring some advantages and it can contribute to improving the quality of the DSM.

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“…The current methods for shadow detection can be divided into three types [11][12][13]: (1) property-based methods [9,[13][14][15][16][17][18][19][20]; (2) geometrical methods [14,[17][18][19][20]; and (3) machine learning methods [15,16,21,22].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

Wang

Yuan

et al. 2017

Remote Sensing

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Abstract:The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods.

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A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data

Cited by 57 publications

References 14 publications

Shadowdetection From VHR Aerial Images in Urban Area by Using 3d City Models and a Decision Fusion Approach

Shadowdetection From VHR Aerial Images in Urban Area by Using 3d City Models and a Decision Fusion Approach

Exploiting the Redundancy of Multiple Overlapping Aerial Images for Dense Image Matching Based Digital Surface Model Generation

An Automatic Shadow Detection Method for VHR Remote Sensing Orthoimagery

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