A novel segmentation method for high-resolution multispectral remote sensing images based on multifeatures

2014 ◽  
Author(s):  
Chao Wang ◽  
Jianqiang Gao ◽  
Chen He ◽  
Fengchen Huang ◽  
Lizhong Xu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Multispectral Remote Sensing

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

2018 ◽  
Vol 8 (10) ◽  
pp. 1883 ◽  
Author(s):  
Hongyin Han ◽  
Chengshan Han ◽  
Xucheng Xue ◽  
Changhong Hu ◽  
Liang Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Near Infrared ◽  
Shadow Detection ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Multispectral Remote Sensing ◽  
The Difference ◽  
Mixed Property ◽  
Very High

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.

scholarly journals Research on Multifeature Segmentation Method of Remote Sensing Images Based on Graph Theory

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Wenxing Bao ◽  
Xiuhong Yao
Keyword(s):  
Remote Sensing ◽  
Graph Theory ◽  
High Resolution ◽  
Texture Features ◽  
Original Image ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Spectrum Shape ◽  
Segmentation Accuracy ◽  
Matching Degree

According to the characteristics of high-resolution remote sensing (RS) images, a new multifeature segmentation method of high-resolution remote sensing images combining the spectrum, shape, and texture features based on graph theory is presented in the paper. Firstly, the quadtree segmentation method is used to partition the original image. Secondly, the spectrum, shape, and texture weight components are calculated all based on the constructed graph. The matching degree between pixels and the texture is computed similarity. Finally, the ratio cut standards combination of the spectrum, shape, and texture weight components is used for the final segmentation. The experimental results show that this method can obtain more ideal results and higher segmentation accuracy applied to RS image than those traditional methods.

scholarly journals Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices

2022 ◽  
Vol 14 (2) ◽  
pp. 326
Author(s):  
Ke Wang ◽  
Hainan Chen ◽  
Ligang Cheng ◽  
Jian Xiao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Land Cover ◽  
Spectral Index ◽  
Remote Sensing Image ◽  
Spectral Indices ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Watershed Transformation ◽  
Multispectral Remote Sensing

Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on the scales of different ground objects. To guarantee image segmentation of ground objects with their own scale information, spectral indices that can be used to enhance some types of land cover, such as green cover and water bodies, were introduced into marker generation for the watershed transformation. First, a vector field model was used to determine the gradient of a multispectral remote-sensing image, and a marker was generated from the gradient. Second, appropriate spectral indices were selected, and the kernel density estimation was used to generate spectral-index marker images based on the analysis of spectral indices. Third, a series of mathematical morphology operations were used to obtain a combined marker image from the gradient and the spectral index markers. Finally, the watershed transformation was used for image segmentation. In a segmentation experiment, an optimal threshold for the spectral-index-marker generation method was identified. Additionally, the influence of the scale parameter was analyzed in a segmentation experiment based on a five-subset dataset. The comparative results for the proposed method, the commonly used watershed segmentation method, and the multiresolution segmentation method demonstrate that the proposed method yielded multispectral remote-sensing images with much better performance than the other methods.

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