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.
Highlights
More complex details of land covers are obtained from high spatial resolution (HSR) multispectral satellite remote sensing images whichAppl
The impact of the logarithmic operation is analyzed by comparing the performance distinction between shadow detection results respectively by the initial shadow index and the logarithmic shadow index in several variant color spaces (i.e., HIS, HSV, CIELCh, YCbCr and YIQ) in the additional experiments with test images Tripoli-1 and Tripoli-2
Stable and high values of the overall accuracy measurement are obtained for test images of WV3-Tripoli, WV3-Rio and WV2-WDC in HIS, HSV, CIELCh and YIQ spaces, the logarithmic shadow index (LSI) method fails in detecting shadow in six test images of WV3-Rio in the YCbCr space
Summary
More complex details of land covers (e.g., buildings, towers, vegetation, farms and roads) are obtained from high spatial resolution (HSR) multispectral satellite remote sensing images whichAppl. Additional cues are obtained from the HSR images with the palpable shadow, such as the general shape and structure of cast objects, the illumination direction and the position of the sun, as well as parameters of the satellite sensor. These cues are helpful in numerous applications, like building detection, height estimation, 3D reconstruction, change surveillance, scene interpretation and position estimation of the sun and satellites [4,5,8,9,10,11,12]. Given either the useful or troublesome influence of shadow in HSR multispectral satellite remote sensing images, in order to improve the utilization of HSR multispectral satellite remote sensing images, shadow detection is an important scientific issue for HSR multispectral remote sensing images, which is usually the first step followed by shadow compensation and image utilization [9,12,14]
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