Detection of and compensation for shadows in colored urban aerial images

Huang, Jianjun; Xie, Weixin; Tang, Liang

doi:10.1109/wcica.2004.1343090

Cited by 29 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To detect the shadows, various thresholds were employed and applied to the ratio images that were generated. Tsai's approach outperformed Polidorio et al [7] and Huang et al [9] and other existing strategies in this comparison. To improve on Tsai's earlier work, Chung et al suggested a new method [10].…”

Section: Introductionmentioning

confidence: 61%

See 1 more Smart Citation

Optimizing the Value of the Ratio Image to Improve Shadow Detection in Satellite Images of Earth

Sarfraz,

Musleh,

Raafat

2023

Preprint

View full text Add to dashboard Cite

Nowadays, there are a ton of diverse applications that use digital images, notably for earth science. Shadows, for example, might cause some distortions in digital images. In digital images, shadows are a typical occurrence that is regarded as a source of noise in many applications. Shadow results in inaccurate image segmentation, the loss of crucial data, and the degradation of image colors. Therefore, it is crucial to find and eliminate shadows from images. This study shows new advancements in the pixel intensity-based shadow detection method for aerial images of the earth. In the ratio image, which is the ratio of hue over the intensity component of the invariant color models, shadow pixels are brighter than nonshadow pixels. The ratio image output pixels values of our existing method [37] for shadow detection are proposed to be exploited and optimized. Two novel testing algorithms, A and B, were designed to test the claimed changes. While our previous algorithm ratio image was given the average value of the input red, green, and blue (RGB) image components pixels, the new algorithm ratio image is given the minimum and maximum values, respectively, of the input RGB image components pixels. The most accurate algorithm, Algorithm A, was chosen to be the improved algorithm. The input RGB aerial image is transformed into the modified invariant color model hue, saturation, and value (HSV) in step one of algorithm A by selecting the pixels with the lowest intensity value from each input RGB image component in order to maximize the ratio of the image's pixel values. The mathematical power function is then applied to the ratio image to greatly increase the difference in pixel values once the ratio image of hue over value has been produced. The optimum ratio image is then classified using a threshold into pixels with and without shadows. The threshold can more accurately help to categorize pixels as shadow or nonshadow. In this work, we compare the proposed algorithms with our existing algorithm in [37] and some existing methods. Algorithm A has the best results among the proposed algorithms and our existing algorithm in [37], in addition to the testing findings, which show that algorithm A has the ability to detect shadows accurately.

show abstract

Section: Introductionmentioning

confidence: 61%

“…Different attributes are used by shadow detection systems in digital images, as seen in [1], [8][9][10], [12][13], [22], [25], and [34][35][36]. Some techniques make use of the geometrical characteristics of the objects in the image that block the light and cast a shadow, while others make use of the light's direction in the image.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Value of the Ratio Image to Improve Shadow Detection in Satellite Images of Earth

Sarfraz,

Musleh,

Raafat

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The method employs Planck's blackbody irradiance theory to estimate the spectral power distributions of daylight and skylight, allowing shadows to be detected without prior knowledge. Huang et al [21] introduced a model that identifies shadows based on their high hue values compared to those of non-shadowed areas. This method utilizes a thresholding strategy within the hue, saturation, and intensity (HSI) color space to accurately differentiate shadowed regions from their surroundings.…”

Section: Shadow Detectionmentioning

confidence: 99%

Adaptive Shadow Compensation Method in Hyperspectral Images via Multi-Exposure Fusion and Edge Fusion

Meng,

Li,

Huang

2024

Applied Sciences

View full text Add to dashboard Cite

Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a series of exposure images through adaptive exposure coefficients that reflect spatial shadow intensity variations. Fusion weights for exposure images are determined based on exposure, contrast, and spectral variance. Then, the exposure sequence and fusion weights are constructed as Laplacian pyramids and Gaussian pyramids, respectively, to obtain a weighted fused exposure sequence. In the second stage, the previously identified shadow regions are smoothly reintegrated into the original image using edge fusion based on the p-Laplacian operator. To further validate the effectiveness and spectral fidelity of our method, we introduce a new hyperspectral image dataset. Experimental results on the public dataset and proposed dataset demonstrate that our method surpasses other mainstream shadow compensation methods.

show abstract

“…The selected feature should highlight the shadow information and be positive to the image segmentation to obtain objects with the closed boundary. Previous studies 38,39,55 have shown that (1) the brightness (intensity) of the shadow area is low due to the occlusion of sunlight; (2) the atmospheric Rayleigh scattering effect is sensitive to the short-wavelength blue and violet bands, but insensitive to the NIR band; and (3) the C3 component of the shadow area has a high value and a high saturation. Therefore, six image features were selected from C1C2C3, the HSV color space, and the remote sensing index: C3, RATIO H_V , RATIO S_V , NIR, the normalized difference vegetation index (NDVI), and the visible atmospherically resistant index (VARI).…”

Section: Feature Selection and Extractionmentioning

confidence: 99%

Object-oriented detection of building shadow in TripleSat-2 remote sensing imagery

Liu

Wei

Tao

et al. 2020

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

The projection of objects on the earth's surface caused by the sunlight produces shadows. They are inevitable in high-spatial-resolution satellite remote sensing images and reduce the accuracy of change detection, land cover classification, target recognition, and many more applications. Dark-colored land covers in these satellite images, such as water bodies, road, and soil, appear with similar spectral properties as those of shadows and often result in difficulty in shadow detection, especially in complex urban settings. We propose an object-oriented building shadow extraction method and tested it using six selected study areas from TripleSat-2 satellite imagery with 3.2-m spatial resolution. The method's main steps include (1) selecting six image features that can highlight the shadow information and then segment the image based on edge; (2) extracting shadow region based on multiple object features; and (3) masking nonbuilding shadow regions by the shadow and dark object separation index, image features including spectral, textural, and geometric features, and contextual information. The average precision, recall, and F1-score of the shadow detection were 85.6%, 88.6%, and 87.0%, respectively, and the ranges were 73.0% to 91.0%, 76.6% to 94.1%, and 74.7% to 91.2%, respectively. Compared with multiscale segmentation, edge-based segmentation is more efficient and helpful to completely and accurately extract shadows. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

Detection of and compensation for shadows in colored urban aerial images

Cited by 29 publications

References 6 publications

Optimizing the Value of the Ratio Image to Improve Shadow Detection in Satellite Images of Earth

Optimizing the Value of the Ratio Image to Improve Shadow Detection in Satellite Images of Earth

Adaptive Shadow Compensation Method in Hyperspectral Images via Multi-Exposure Fusion and Edge Fusion

Object-oriented detection of building shadow in TripleSat-2 remote sensing imagery

Contact Info

Product

Resources

About