Near real-time shadow detection and removal in aerial motion imagery application

Silva, Guilherme F.; Carneiro, Grace B.; Doth, Ricardo Vinicius; Amaral, Leonardo Albernaz; Azevedo, Dario Francisco Guimaraes de

doi:10.1016/j.isprsjprs.2017.11.005

Cited by 60 publications

(69 citation statements)

References 22 publications

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“…20. Another aspect addressing color space processing is that other nonconventional spaces such as C1C2C3 6,21 and CIELAB 22 have been successfully tested for shadow detection, as the detectors take advantage of the shadow properties in color spaces that are invariant with respect to (w.r.t.) radiance and chromaticity.…”

Section: And 12mentioning

confidence: 99%

“…Despite the benefits of using invariant color models, radiometric corrections are not properly managed by methods purely inspired on color properties, hence making these methods very sensitive to sky illumination, misclassification, and false detection. 4,6,22,23 Detecting shadows in VHR images is a challenging task and it remains an open problem for dense environments where the discrimination of the objects from the scene is very critical and difficult to be performed in practice, especially by nonsupervised paradigms. 4,12 A robust method should guarantee independency of the material reflectance and a low necessity of additional input data, being also capable of handling spectral features and contextual information simultaneously.…”

Section: And 12mentioning

confidence: 99%

“…In many cases, morphological filtering operators have been used meanly to get rid of noisy and isolated regions, 10,33,34 and for smoothing the shape objects. 22 Conversely, BTH can also be applied to detect shadows effectively, as the technique works well to tag valleys. It consists of taking regional minima from the residuals of the arithmetic difference between closing transformation and the input image.…”

Section: Uiqimentioning

confidence: 99%

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Shadow detection using object area-based and morphological filtering for very high-resolution satellite imagery of urban areas

Azevedo

Silva

Colnago

et al. 2019

J. Appl. Rem. Sens.

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The presence of shadows in remote sensing images leads to misinterpretation of objects and a wrong discrimination of the targets of interest, therefore, limiting the use of several imaging applications. An automatic area-based approach for shadow detection is proposed, which combines spatial and spectral features into a unified and flexible approach. Potential shadow-pixels candidates are identified using morphological-based operators, in particular, black-top-hat transformations as well as area injunction strategies as computed by the well-established normalized saturation-value difference index. The obtained output is a shadow mask, refined in the last step of our method in order to reduce misclassified pixels. Experiments over a large dataset formed by more than 200 scenes of very high-resolution images covering the metropolitan urban area of São Paulo city are performed, where the images are collected from the WorldView-2 (WV-2) and Pléiades-1B (PL-1B) sensors. As verified by an extensive battery of tests, the proposed method provides a good level of discrimination between shadow and nonshadow pixels, with an overall accuracy up to 94.2%, for WV-2, and 90.84%, for PL-1B. Comparative results also attested that the designed approach is very competitive against representative state-of-the-art methods and it can be used for further shadow removal-dependent applications.

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Section: And 12mentioning

confidence: 99%

Section: And 12mentioning

confidence: 99%

Section: Uiqimentioning

confidence: 99%

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Shadow detection using object area-based and morphological filtering for very high-resolution satellite imagery of urban areas

Azevedo

Silva

Colnago

et al. 2019

J. Appl. Rem. Sens.

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“…Inspired by the STS procedure by Chung et al [23]. Silva et al [24] extended the SRI method by Tsai [20] specifically in the CIELCh color space by applying a natural logarithm function to the original ratio map to compress the original values, resulting in the logarithmic spectral ratio index (LSRI) algorithm. Then, the ratio map was segmented by applying multilevel thresholding.…”

Section: Introductionmentioning

confidence: 99%

Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces

Han

Lan

et al. 2020

Applied Sciences

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Shadow often results in difficulties for subsequent image applications of multispectral satellite remote sensing images, like object recognition and change detection. With continuous improvement in both spatial and spectral resolutions of satellite remote sensing images, a more serious impact occurs on satellite remote sensing image interpretation due to the existence of shadow. Though various shadow detection methods have been developed, problems of both shadow omission and nonshadow misclassification still exist for detecting shadow well in high-resolution multispectral satellite remote sensing images. These shadow detection problems mainly include high small shadow omission and typical nonshadow misclassification (like bluish and greenish nonshadow misclassification, and large dark nonshadow misclassification). For further resolving these problems, a new shadow index is developed based on the analysis of the property difference between shadow and the corresponding nonshadow with several multispectral band components (i.e., near-infrared, red, green and blue components) and hue and intensity components in various invariant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ), respectively. The shadow mask is further acquired by applying an optimal threshold determined automatically on the shadow index image. The final shadow image is further optimized with a definite morphological operation of opening and closing. The proposed algorithm is verified with many images from WorldView-3 and WorldView-2 acquired at different times and sites. The proposed algorithm performance is particularly evaluated by qualitative visual sense comparison and quantitative assessment of shadow detection results in comparative experiments with two WorldView-3 test images of Tripoli, Libya. Both the better visual sense and the higher overall accuracy (over 92% for the test image Tripoli-1 and approximately 91% for the test image Tripoli-2) of the experimental results together deliver the excellent performance and robustness of the proposed shadow detection approach for shadow detection of high-resolution multispectral satellite remote sensing images. The proposed shadow detection approach is promised to further alleviate typical shadow detection problems of high small shadow omission and typical nonshadow misclassification for high-resolution multispectral satellite remote sensing images.

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“…The popular methods used to detect cloud shadows can be grouped into two classes: (1) geometry-based methods (Choi & Bindschadler, 2004;Huang et al, 2010;Luo et al, 2008;Simpson et al, 2000;Simpson & Stitt, 1998), in which the geometric relations of the Sun, clouds, and satellite are used to determine the positions and distributions of the shadows. In many cases, it is not an easy work, especially the accurate estimation of the cloud top height; (2) spectral-based methods (Ackerman et al, 1998;Huang et al, 2010;Irish et al, 2006;Silva et al, 2018;Zhu & Woodcock, 2012;Zhu & Woodcock, 2014). Similar to the cloud detection methods mentioned above, this type of methods depends on the differences in multispectral characteristics or different color spaces of shadows in reflective and/or thermal bands.…”

Section: Introductionmentioning

confidence: 99%

Detection and Removal of Clouds and Associated Shadows in Satellite Imagery Based on Simulated Radiance Fields

et al. 2019

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Clouds and shadows pose a significant barrier for land surface optical and infrared remote sensing image processing and their various applications. The detection and removal of clouds and shadows from satellite images have always been critical preprocessing steps. To date, a variety of methods have been designed to solve this problem. Some require particular channels, while others are heavily dependent on the availability of temporally adjacent images (reference images). Moreover, many methods are too complex to use by common users. For those reasons, in this paper an alternative scheme for detecting clouds and shadows is proposed based on simulated top‐of‐atmosphere radiance fields. At the same time, a simple approach to remove clouds and shadows is also provided. The results indicate that the new method can properly identify both clouds and shadows in satellite images. Especially, it shows obvious advantage over the Moderate Resolution Imaging Spectroradiometer cloud product (MOD35) for shadow detection. Although the proposed cloud removal method is simple, the radiances of a contaminated image can be reasonably reconstructed with root‐mean‐square error < 3.0 W/m2·sr·μm and mean bias < 1.0 W/m2·sr·μm for all seven Moderate Resolution Imaging Spectroradiometer reflective bands for our case studies. These results prove the effectiveness of the proposed scheme in identifying and removing clouds and shadows from remotely sensed images. Meanwhile, these findings provide some new ideas for the remote sensing community, especially in the fields of cloud detection and image processing.

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Near real-time shadow detection and removal in aerial motion imagery application

Cited by 60 publications

References 22 publications

Shadow detection using object area-based and morphological filtering for very high-resolution satellite imagery of urban areas

Shadow detection using object area-based and morphological filtering for very high-resolution satellite imagery of urban areas

Automatic Shadow Detection for Multispectral Satellite Remote Sensing Images in Invariant Color Spaces

Detection and Removal of Clouds and Associated Shadows in Satellite Imagery Based on Simulated Radiance Fields

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