A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

Abstract

Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.

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

“…In accordance with the Phong illumination model [19] and contributions in other studies [14,15,20,28], compared with nonshadow regions, similar ground objects in shadow regions often obviously possess the following properties:…”

“…Chromaticity and luminance are powerful descriptors for color images [28]. The appropriate description of both chromaticity and luminance simplifies image characteristic extraction and image interpretation [29].…”

“…In addition to the often utilized R, G and B components of the target image, NIR information attracts more attention ever than before along with the spectral resolution improvement of HSR remote sensing images by recently launched optical satellites [6,28,33]. Theoretically, in accordance with the Phong's illumination model [19] and the Huang's imaging model [15], the diffusion part of the incident light maintains the difference between shadow and nonshadow.…”

“…where "+1" is aimed at avoiding the calculation of ln(0). Additionally, a significant importance appears in real-time and approximate real-time image processing (taking the shadow detection as an example) for HSR satellites [14,28]. A great attention is attached to the timesaving shadow detection algorithm for shadow processing on HSR satellites.…”

Algorithms for Enhancing Satellite Imagery to Discover Archaeological Finds Covered by Shadow

“…For multispectral images, the parameters of chromaticity (hue component) and luminance (intensity component) in the invariant color space were considered, as well as the reflectivity of the bands themselves to highlight the shadow zones. For these reasons, it has been started with the application of Mixed Property-Based Automatic Shadow (MSPI) [ 24 ]. The processed image will then have higher values in shadow pixels than in non-shadow ones and the vegetation cover in non-shadow areas, applying the optimal shadow index threshold recommended by Otsu [ 20 ].…”