Shadow detection and removal using a shadow formation model

Shedlovska, Y. I.; Hnatushenko, Volodymyr

doi:10.1109/dsmp.2016.7583537

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…For example, Zhang et al [14] andZigh et al [15]separated shadow from non‐shadow by thresholding on red, green, and blue(RGB) colour space. Kantsingh et al [16] detected shadow by considering the hue, saturation,and value (HSV) colour space using the OTSU method for thresholding.Shedlovska and Hnatushenko [17] firstobtained the spectral ratio image r ( x )from HSV colour space, and then applied the OTSU thresholding method overthe r ( x ) histogram. More recently, Anoopa et al [18] andSu et al [19] usedthe dual pass OTSU method to detect shadow for urban high‐resolutionsatellite images.…”

Section: Related Workmentioning

confidence: 99%

Property‐based shadow detection and removal method for licence plate image

Gao

et al. 2020

IET Image Processing

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Section: Related Workmentioning

confidence: 99%

Property‐based shadow detection and removal method for licence plate image

Gao

et al. 2020

IET Image Processing

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“…For the third and fourth requirements, the tendency is always the higher, the better. However, the higher spatial resolution also entails specific problems, for example, a more powerful hardware system, longer processing times that can be handled by cloud computing (Amani et al, 2020), shadows in cities (Shedlovska & Hnatushenko, 2016), the cost of commercial satellite images (Shean et al, 2016). The frequency of the data primarily depends on satellite revisit time but usually for monitoring multi‐annual war environment impacts, the optimal frequency for the analysis is from one image for every year, up to 1 image every 2.5, 5, or 10 years.…”

Section: Introductionmentioning

confidence: 99%

Monitoring war‐generated environmental security using remote sensing: A review

et al. 2022

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The negative impact humans have on the environment directly affects the environmental security. Over the years, it has been proven that wars make drastic and sometimes unrecoverable environmental damage. Remote sensing has played a main role in providing necessary data for spatiotemporal analysis. This paper reviews remote sensing implementation of war activities for environmental monitoring. This review is timely due to the exponentially increasing number of works published in recent years. This paper's main objective is to locate the papers and find geographic link, sensor use, and environmental degradation type. Following a discussion of remote sensing's capabilities, this overview illustrates numerous environmental damages caused by military operations in various world places. Our study found that wars have a detrimental influence on the ecosystem across the world, with major reasons being forest loss, oil spills, and urban growth. According to the findings, remote sensing, particularly middle‐resolution satellite images, is extensively and successfully employed for environmental security monitoring. The rehabilitation of a deteriorated environment should be one of the key areas of future study.

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“…Numerous methods attending to shadow removal in outdoor scenes have been developed. One of the leading strategies applied when performing shadow removal in outdoor scenes is illumination correction [25][26][27]. Highlighted examples in published works are methods that execute shadow removal in UAV aerial images using retinex theory [28] as the basis of computing the illumination correction, wherein the work published by Guo et al [29] computes an improved luminance image and executes an illumination correction based upon the color transfer formula.…”

Section: Introductionmentioning

confidence: 99%

A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

et al. 2021

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Through the increasing use of unmanned aerial vehicles as remote sensing tools, shadows become evident in aerial imaging; this fact, alongside the higher spatial resolution obtained by high-resolution mounted cameras, presents a challenging issue when performing different image processing tasks related to urban areas monitoring. Accordingly, the state-of-the-art reported works can correct the shadow regions, but the heterogeneity between the corrected shadow and non-shadow areas is still evident and especially noticeable in concrete and asphalt regions. The present work introduces a local color transfer methodology to shadow removal which is based on the CIE L*a*b (Lightness, a and b) color space that considers chromatic differences in urban regions, and it is followed by a color tuning using the HSV color space. The quantitative comparison was executed by using the shadow standard deviation index (SSDI), where the proposed work provided low values that improve up to 19 units regarding other tested methods. The qualitative comparison was visually realized and proved that the proposed method enhances the color correspondence without losing texture information. Quantitative and qualitative results validate the results of color correction and texture preservation accuracy of the proposed method against other published methodologies.

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Shadow detection and removal using a shadow formation model

Cited by 13 publications

References 9 publications

Property‐based shadow detection and removal method for licence plate image

Property‐based shadow detection and removal method for licence plate image

Monitoring war‐generated environmental security using remote sensing: A review

A Novel Shadow Removal Method Based upon Color Transfer and Color Tuning in UAV Imaging

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