Removing Shadows Using RGB Color Space in Pairs of Optical Satellite Images

Zigh, Ehlem; Kouninef, Belkacem; Kadiri, Mohammed

doi:10.1007/s12524-016-0598-x

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…In order to mitigate the interference of shadow on remote sensing images and improve the efficiency of utilization of images, researchers are trying to improve shadow processing technology of remote sensing images from various angles [ 10 , 11 , 12 , 13 , 14 , 15 ]. Our work is a new attempt to reform the shadow images by combining the Atmospheric Physical Transmission model with the generation of confrontation network.…”

Section: Related Workmentioning

confidence: 99%

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Wang

2022

Entropy

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Shadow is one of the fundamental indicators of remote sensing image which could cause loss or interference of the target data. As a result, the detection and removal of shadow has already been the hotspot of current study because of the complicated background information. In the following passage, a model combining the Atmospheric Transport Model (hereinafter abbreviated as ATM) with the Poisson Equation, AP ShadowNet, is proposed for the shadow detection and removal of remote sensing images by unsupervised learning. This network based on a preprocessing network based on ATM, A Net, and a network based on the Poisson Equation, P Net. Firstly, corresponding mapping between shadow and unshaded area is generated by the ATM. The brightened image will then enter the Confrontation identification in the P Net. Lastly, the reconstructed image is optimized on color consistency and edge transition by Poisson Equation. At present, most shadow removal models based on neural networks are significantly data-driven. Fortunately, by the model in this passage, the unsupervised shadow detection and removal could be released from the data source restrictions from the remote sensing images themselves. By verifying the shadow removal on our model, the result shows a satisfying effect from a both qualitative and quantitative angle. From a qualitative point of view, our results have a prominent effect on tone consistency and removal of detailed shadows. From the quantitative point of view, we adopt the non-reference evaluation indicators: gradient structure similarity (NRSS) and Natural Image Quality Evaluator (NIQE). Combining various evaluation factors such as reasoning speed and memory occupation, it shows that it is outstanding among other current algorithms.

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Section: Related Workmentioning

confidence: 99%

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

Wang

2022

Entropy

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“…Most property‐based methods use threshold techniques to determine shadows. 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.…”

Section: Related Workmentioning

confidence: 99%

“…Unfortunately, some false shadow will be detected based on the above simple threshold techniques such as false shadows of dark objects. So, Zhang et al [14] considered the shape, size [20, 21], and spatial [14, 15] properties of the shadow to improve detection results. At the same time, taking advantage of the inherent sensitivity of digital camera sensors, the near‐infrared (NIR) images are introduced to solve the above problem [22, 23].…”

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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“…The Euclidean clustering algorithm has a poor effect on the separation of adjacent ground object point clouds, and the region-growing algorithm is only suitable for the separation of regular columns. The red-green-blue (RGB) [31,32] and luminance-bandwidth-chrominance (YUV) [33] color spaces are commonly used; the RGB color space is a linear combination of color attributes, and the YUV color space is obtained by converting RGB attribute values based on the sensitivity of luminance attributes over chrominance attributes. Each region in the color space corresponds to a chromaticity attribute and a saturation attribute, which are used to separate the regions independently of the luminance attribute.…”

Section: Introductionmentioning

confidence: 99%

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

et al. 2022

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Oil tea (Camellia oleifera) is one of the world’s major woody edible oil plants and is vital in providing food and raw materials and ensuring water conservation. The yield of oil tea can directly reflect the growth condition of oil tea forests, and rapid and accurate yield measurement is directly beneficial to efficient oil tea forest management. Light detection and ranging (LiDAR), which can penetrate the canopy to acquire the geometric attributes of targets, has become an effective and popular method of yield identification for agricultural products. However, the common geometric attribute information obtained by LiDAR systems is always limited in terms of the accuracy of yield identification. In this study, to improve yield identification efficiency and accuracy, the red-green-blue (RGB) and luminance-bandwidth-chrominance (i.e., YUV color spaces) were used to identify the point clouds of oil tea fruits. An optimized mean shift clustering algorithm was constructed for oil tea fruit point cloud extraction and product identification. The point cloud data of oil tea trees were obtained using terrestrial laser scanning (TLS), and field measurements were conducted in Changsha County, central China. In addition, the common mean shift, density-based spatial clustering of applications with noise (DBSCAN), and maximum–minimum distance clustering were established for comparison and validation. The results showed that the optimized mean shift clustering algorithm achieved the best identification in both the RGB and YUV color spaces, with detection ratios that were 9.02%, 54.53%, and 3.91% and 7.05%, 62.35%, and 10.78% higher than those of the common mean shift clustering, DBSCAN clustering, and maximum-minimum distance clustering algorithms, respectively. In addition, the improved mean shift clustering algorithm achieved a higher recognition rate in the YUV color space, with an average detection rate of 81.73%, which was 2.4% higher than the average detection rate in the RGB color space. Therefore, this method can perform efficient yield identification of oil tea and provide a new reference for agricultural product management.

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Removing Shadows Using RGB Color Space in Pairs of Optical Satellite Images

Cited by 6 publications

References 22 publications

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

AP Shadow Net: A Remote Sensing Shadow Removal Network Based on Atmospheric Transport and Poisson’s Equation

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

Identification of the Yield of Camellia oleifera Based on Color Space by the Optimized Mean Shift Clustering Algorithm Using Terrestrial Laser Scanning

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