A Remote Sensing Image Segmentation Method Based on Spectral and Texture Information Fusion

2009 ◽  
Author(s):  
Xing Xie ◽  
Mengliang Liu ◽  
Leiguang Wang ◽  
Qianqin Qin
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Information Fusion ◽  
Remote Sensing Image ◽  
Segmentation Method ◽  
Texture Information

HIGH-RESOLUTION REMOTE SENSING IMAGE SEGMENTATION METHOD BASED ON SReLU

2019 ◽  
Vol 34 (3) ◽  
Author(s):  
Chenming Li ◽  
Xiaoyu Qu ◽  
Yao Yang ◽  
Hongmin Gao ◽  
Yongchang Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Segmentation Method

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.

scholarly journals An Improved Hybrid Segmentation Method for Remote Sensing Images

2019 ◽  
Vol 8 (12) ◽  
pp. 543
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Yongji Wang ◽  
Qingwen Qi
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Schedule Algorithm ◽  
Remote Sensing Image ◽  
Segmentation Method ◽  
Common Boundary ◽  
Remote Sensing Image Classification ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Split And Merge

Image segmentation technology, which can be used to completely partition a remote sensing image into non-overlapping regions in the image space, plays an indispensable role in high-resolution remote sensing image classification. Recently, the segmentation methods that combine segmenting with merging have attracted researchers’ attention. However, the existing methods ignore the fact that the same parameters must be applied to every segmented geo-object, and fail to consider the homogeneity between adjacent geo-objects. This paper develops an improved remote sensing image segmentation method to overcome this limitation. The proposed method is a hybrid method (split-and-merge). First, a watershed algorithm based on pre-processing is used to split the image to form initial segments. Second, the fast lambda-schedule algorithm based on a common boundary length penalty is used to merge the initial segments to obtain the final segmentation. For this experiment, we used GF-1 images with three spatial resolutions: 2 m, 8 m and 16 m. Six different test areas were chosen from the GF-1 images to demonstrate the effectiveness of the improved method, and the objective function (F (v, I)), intrasegment variance (v) and Moran’s index were used to evaluate the segmentation accuracy. The validation results indicated that the improved segmentation method produced satisfactory segmentation results for GF-1 images (average F (v, I) = 0.1064, v = 0.0428 and I = 0.17).

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