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

Abstract

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.

A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images

“…The use of the HSV colour space is not new to remote sensing. Some of its applications include cloud and cloud shadow detection [30,31], crop and farmland detection [32,33], algal bloom detection [34], surface water extraction and river extraction [35,36], oceanic water assessments [37] and water quality retrieval [29,38]. Most recently, Lee et al [6] related the true colour hue angle to the vertical variation of light quality in marine waters, as well as the different optical water types, which represent different aquatic environments globally [39,40].…”

Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

“…Another issue in multispectral image reconstruction is the ubiquitous existence of shadows. Image pre-processing includes either transforming images to shadow invariant space, or other deep learning methods to remove the shadows before being used for multispectral image reconstruction [74,75]. Once these strategies are incorporated in current reconstruction models, full spectrum reconstruction processes will likely become more precise and thus automatable.…”

Image pre-processing and shadow detection for building extraction from high-resolution satellite images

“…High spatial resolution (HSR) multispectral satellite remote sensing images, acquired by satellites such as IKONOS, , QuickBird, WorldView-2, WorldView-3, and Jilin-1, provide an effective means of capturing intricate land cover details, encompassing elements like buildings, towers, vegetation, farms, and roads (Han et al, 2020). However, as spatial resolution is heightened, the impact of shadows becomes increasingly pronounced, particularly in urban areas with tall structures like buildings, towers, and trees (Jin et al, 2020).…”

“…When individuals perceive a colored object, they tend to characterize it based on properties encompassing hue, saturation, and brightness. These attributes find representation across several analogous color models, including HSL, HSI, and HSV (representing hue-saturation-intensity/value) (Han et al, 2020). The presence of numerous HSL-like color spaces in the literature, combined with their reliance on specific devices, has led to various authors offering disparate equations to define the same color space.…”

“…These challenges chiefly encompass the significant omission of small shadows and the erroneous classification of typical non-shadow regions, manifesting as misclassifications of bluish and greenish dark non-shadow areas, along with substantial misclassifications of expansive dark non-shadow areas. As a result, the task of shadow detection remains intricate and demanding for VHSR multispectral satellite remote sensing images (Han et al, 2020).…”