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.

This research mainly expounds upon the decision-level software defect prediction theory. The defect characteristics is the first research focus. For the first research focus, a characteristic comparison set is built out of the existing defect characteristics according to the dissimilarity of defect characteristics and the defect characteristics are organized outside the characteristic comparison set into some defect characteristic clusters to reduce the scale of the characteristic data. The defects is the second research focus. For the second research focus, the vector weights are assigned to the defect characteristics contained in the defects according to the minimum critical characteristic set. Moreover, the multi-agent algorithm integration technology is used to predict defects according to the repulsive relationship between similar defect clusters.