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.
Highlights
With the development of aerospace techniques, an increasing number of very high-resolution (VHR) satellites have been launched in recent years, such as Ikonos, QuickBird, Pleiades, GeoEye, RapidEye, Skysat-1, WorldView-2, WorldView-3, Jilin-1, and Kompsat [1,2,3,4,5,6,7,8,9,10]
We describe the experiment conditions and test images used for comparative comparative experiments detection approach, as as well as experiments to to demonstrate demonstratethe theperformance performanceofofour ourdeveloped developedshadow shadow detection approach, well other comparable shadow detection approaches for different images,test andimages, state the and corresponding as other comparable shadow detection approaches for test different state the shadow detection results
We developed and validated a new shadow detection approach based on general shadow properties including high hue and low intensity in the HSV invariant color space and special spectral reflectivity features of high reflectivity values in the near infrared band and low reflectivity values in the red band for vegetation in nonshadow regions
Summary
With the development of aerospace techniques, an increasing number of very high-resolution (VHR) satellites have been launched in recent years, such as Ikonos, QuickBird, Pleiades, GeoEye, RapidEye, Skysat-1, WorldView-2, WorldView-3, Jilin-1, and Kompsat [1,2,3,4,5,6,7,8,9,10]. The VHR multispectral remote sensing images captured by these satellites can depict more details of typical land cover, including buildings, vegetation and roads. Along with the improvement in the optical spatial resolution, shadow interference in these multispectral remote sensing images has become more serious. Shadow analysis is more important than ever before for these VHR multispectral remote sensing image applications. Shadow occurs when ground objects are illuminated by the sun or other light sources.
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