An adaptive multi-threshold image segmentation algorithm based on object-oriented classification for high-resolution remote sensing images

2017 ◽  
Author(s):  
Kai Yu ◽  
Jiahang Liu ◽  
Zhuanli Lu
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Object Oriented ◽  
Segmentation Algorithm ◽  
Remote Sensing Images ◽  
Image Segmentation Algorithm

Dynamic image segmentation algorithm in 3D descriptions of remote sensing images

2015 ◽  
Vol 75 (16) ◽  
pp. 9723-9743 ◽  
Author(s):  
Ching-Yi Chen ◽  
Hsuan-Ming Feng ◽  
Hua-Ching Chen ◽  
Shiang-Min Jou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Remote Sensing Images ◽  
Dynamic Image ◽  
Image Segmentation Algorithm

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

2018 ◽  
Vol 46 (11) ◽  
pp. 1805-1814
Author(s):  
Tianjun Wu ◽  
Liegang Xia ◽  
Jiancheng Luo ◽  
Xiaocheng Zhou ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Computationally Efficient ◽  
Remote Sensing Images

scholarly journals STUDY ON BUILDING EXTRACTION FROM HIGH-RESOLUTION IMAGES USING MBI

2018 ◽  
Vol XLII-3 ◽  
pp. 283-287 ◽  
Author(s):  
Z. Ding ◽  
X. Q. Wang ◽  
Y. L. Li ◽  
S. S. Zhang
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Extraction Methods ◽  
Research Topic ◽  
False Alarms ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Shape Constraint ◽  
High Resolution Images

Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.

Remote Sensing Image Segmentation Based on Improved Statistical Region Merging

2014 ◽  
Vol 667 ◽  
pp. 226-229
Author(s):  
Xiu Li Gong ◽  
Zhi Ming Wang
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Remote Sensing Image ◽  
Segmentation Algorithm ◽  
Region Merging ◽  
Gradient Estimation ◽  
Coarse To Fine ◽  
Statistical Region Merging ◽  
Image Segmentation Algorithm ◽  
Dissimilarity Criterion

Statistical Region Merging (SRM) is an efficient image segmentation algorithm for images with noise and partial occlusion. However, due to the complexity of remote sensing image, SRM can’t give satisfactory results. This paper proposes an improved image segmentation algorithm for remote sensing image based on SRM. Firstly, 8-connexity gradient estimation models are used to obtain more precisely edges. Secondly, the dissimilarity criterion between regions is replaced by a normalized distance standard. Finally, it dynamically updates and sorts dissimilarity between regions during region merging. Experimental results show the proposed algorithm can achieve better segmentation results from coarse to fine compared with original SRM.

scholarly journals Improved CNN Classification Method for Groups of Buildings Damaged by Earthquake, Based on High Resolution Remote Sensing Images

2020 ◽  
Vol 12 (2) ◽  
pp. 260 ◽  
Author(s):  
Haojie Ma ◽  
Yalan Liu ◽  
Yuhuan Ren ◽  
Dacheng Wang ◽  
Linjun Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Image Segmentation ◽  
High Resolution ◽  
Test Accuracy ◽  
Remote Sensing Images ◽  
Damage Degree ◽  
Learning Classifier ◽  
Object Oriented Methods

Effective extraction of disaster information of buildings from remote sensing images is of great importance to supporting disaster relief and casualty reduction. In high-resolution remote sensing images, object-oriented methods present problems such as unsatisfactory image segmentation and difficult feature selection, which makes it difficult to quickly assess the damage sustained by groups of buildings. In this context, this paper proposed an improved Convolution Neural Network (CNN) Inception V3 architecture combining remote sensing images and block vector data to evaluate the damage degree of groups of buildings in post-earthquake remote sensing images. By using CNN, the best features can be automatically selected, solving the problem of difficult feature selection. Moreover, block boundaries can form a meaningful boundary for groups of buildings, which can effectively replace image segmentation and avoid its fragmentary and unsatisfactory results. By adding Separate and Combination layers, our method improves the Inception V3 network for easier processing of large remote sensing images. The method was tested by the classification of damaged groups of buildings in 0.5 m-resolution aerial imagery after the earthquake of Yushu. The test accuracy was 90.07% with a Kappa Coefficient of 0.81, and, compared with the traditional multi-feature machine learning classifier constructed by artificial feature extraction, this represented an improvement of 18% in accuracy. Our results showed that this improved method could effectively extract the damage degree of groups of buildings in each block in post-earthquake remote sensing images.

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