Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Zhou, Tingting; Fu, Haoyu; Sun, Chenglin; Wang, Shenghan

doi:10.3390/rs13040699

Cited by 48 publications

(39 citation statements)

References 41 publications

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“…In Zhou et al. (2021)'s method, the image of a shadow index with the YCbCr model and the NIR band is constructed and segmented by the mean‐shift algorithm. The values of segmentation spatial radius are obtained by analyzing the features of the ground object.…”

Section: Resultsmentioning

confidence: 99%

“…But the size and shape of shadow areas in the VNIS image are irregular; Tsai's method of preserving the shape of shadow based on edge detection is not well-suited. In Zhou et al (2021)'s method, the image of a shadow index with the YCbCr model and the NIR band is constructed and segmented by the mean-shift algorithm. The values of segmentation spatial radius are obtained by analyzing the features of the ground object.…”

Section: Comparative Studymentioning

confidence: 99%

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A Coarse‐To‐Fine Approach to Detect Shadows in the Chang’E−4 VNIS Hyperspectral Images

Wang

et al. 2022

Earth and Space Science

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The reflectance of the Moon's surface is determined by the factors, such as mineralogical components, the soil's physical properties (e.g., porosity, roughness, and particle size), and the space weathering effect (Hapke, 2012). These factors are of high importance to unveil the geological evolution of Moon. Thus, reflectance is a quantity of high priority that lunar orbiter missions frequently measured (

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Section: Resultsmentioning

confidence: 99%

Section: Comparative Studymentioning

confidence: 99%

A Coarse‐To‐Fine Approach to Detect Shadows in the Chang’E−4 VNIS Hyperspectral Images

Wang

et al. 2022

Earth and Space Science

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“…Avg. computational cost (detection, segmentation) DBDF [38] 7 m, 13 m LBP [21] 5 s, 58 ms Vu [13] 1 m 20 s, 35 s SVD [24] 50 s, 55 s Shi [12] 55 s Zhou [28] 45 s Zhu [22] 12 min Proposed method 7 s International Journal of Optics method in the future that can effectively detect both the motion and focus blur.…”

Section: Dbd Methodsmentioning

confidence: 99%

“…Recently, deep learning methods have been heavily employed in various computer vision and image processing applications, i.e., saliency detection [1], semantic segmentation [2], automatic shadow detection [28] airplane detection using remote sensing images [29], ship detection from real-time images [30], vehicle detection [31], etc. e significance of deep learning algorithms is proven to be effective for defocus blur detection and segmentation, however, at the expense of increased computational cost.…”

Section: Introductionmentioning

confidence: 99%

A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

Khan

Javed

Irtaza

et al. 2021

International Journal of Optics

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Blur detection (BD) is an important and challenging task in digital imaging and computer vision applications. Accurate segmentation of homogenous smooth and blur regions, low-contrast focal regions, missing patches, and background clutter, without having any prior information about the blur, are the fundamental challenges of BD. Previous work on BD has emphasized much effort on designing local sharpness metric maps from the images. However, the smooth/blurred regions having the same patterns as sharp regions make them problematic. This paper presents a robust novel method to extract the local metric map for blurred and nonblurred regions based on multisequential deviated patterns (MSDPs). Unlike the preceding, MSDP extracts the local sharpness metric map on the images at multiple scales using different adaptive thresholds to overcome the problems of smooth/blur regions and missing patches. By using the integral values of the image along with image masking and Otsu thresholding, highly accurate segmented regions of the images are acquired. We argue/hypothesize that the local sharpness map extraction by using direct integral information of the image is highly affected by the threshold selected for distinction between the regions, whereas MSDP feature extraction overcomes the limitations substantially by using automatic threshold computation over multiple scales of the images. Moreover, the proposed method extracts the relatively accurate sharp regions from the high-dense blur and noisy images. Experiments are conducted on two commonly used SHI and DUT datasets for blur and sharp region classifications. The results indicate the effectiveness of the proposed method in terms of sharp segmented regions. Experimental results of qualitative and quantitative comparisons of the proposed method with ten comparative methods demonstrate the superiority of our method. Moreover, the proposed method is also computationally efficient over state-of-the-art methods.

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“…The cast shadow and the self-shadow are two types of shadow (Zhou et al 2021). Here the self-shadow obtains by non-direct facing of an object against the sunlight or any other light.…”

Section: Aerial Image Processingmentioning

confidence: 99%

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

Rani¹,

Pushparaj²,

Raj³

2022

Earth Sci Inform

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In order to produce better images, darkness identification and detection are essential in remote sensing techniques. Throughout this method, shadow attributes are carefully considered during contrast enhancement, and suspicious darkness is always derived based on the aforementioned image features. Furthermore, a few dark artifacts that could still be misinterpreted as shadows are rejected. The whole feature engineering is completely dependent upon the size of the object as well as their relative positions. The precedent set of the inside and outside highlight profile line (IOHPL) can be used to consider removing reflective surfaces from object categories. IOHPL segmental synchronization results in modules that are relatively homogeneous. Following that, the relatively homogenous sections are used to find the percentage light diagnostic specifications between both the shadow as well as non-shadow regions. This process will result in the effective removal of excess shadows or darkness in satellite-derived aerial urban images.

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Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

Cited by 48 publications

References 41 publications

A Coarse‐To‐Fine Approach to Detect Shadows in the Chang’E−4 VNIS Hyperspectral Images

A Coarse‐To‐Fine Approach to Detect Shadows in the Chang’E−4 VNIS Hyperspectral Images

A Robust Approach for Blur and Sharp Regions’ Detection Using Multisequential Deviated Patterns

Escalating the resolution of an urban aerial image via novel shadow amputation algorithm

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