A segmentation method for remote sensing image region on Riemannian manifolds

2015 ◽  
Author(s):  
Hailong Zhu ◽  
Song Zhao ◽  
Xiping Duan
Keyword(s):  
Remote Sensing ◽  
Riemannian Manifolds ◽  
Remote Sensing Image ◽  
Segmentation Method ◽  
Image Region

Cascaded panoptic segmentation method for high resolution remote sensing image

2021 ◽  
pp. 107515
Author(s):  
Xia Hua ◽  
Xinqing Wang ◽  
Ting Rui ◽  
Faming Shao ◽  
Dong Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Segmentation Method

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 A Remote Sensing Image Segmentation Method Based on Fusion Mechanism

2021 ◽  
Vol 2138 (1) ◽  
pp. 012016
Author(s):  
Shuangling Zhu ◽  
Guli Nazi·Aili Mujiang ◽  
Huxidan Jumahong ◽  
Pazi Laiti·Nuer Maiti
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Attention Mechanism ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Input Layer ◽  
Basic Network

Abstract A U-Net convolutional network structure is fully capable of completing the end-to-end training with extremely little data, and can achieve better results. When the convolutional network has a short link between a near input layer and a near output layer, it can implement training in a deeper, more accurate and effective way. This paper mainly proposes a high-resolution remote sensing image change detection algorithm based on dense convolutional channel attention mechanism. The detection algorithm uses U-Net network module as the basic network to extract features, combines Dense-Net dense module to enhance U-Net, and introduces dense convolution channel attention mechanism into the basic convolution unit to highlight important features, thus completing semantic segmentation of dense convolutional remote sensing images. Simulation results have verified the effectiveness and robustness of this study.

An Improved Mean Shift Segmentation Method of High-Resolution Remote Sensing Image Based on LBP and Canny Features

2015 ◽  
Vol 713-715 ◽  
pp. 1589-1592
Author(s):  
Yong Li ◽  
Jing Wen Xu ◽  
Jun Fang Zhao ◽  
Yu Dan Zhao ◽  
Xin Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Feature Space ◽  
Remote Sensing Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
Mean Shift Algorithm ◽  
Segmentation Accuracy ◽  
Fuzzy Boundary

Mean shift algorithm is a robust approach toward feature space analysis, which has been wildly used for natural scene image and medical image segmentation. Due to fuzzy boundary and low accuracy of Mean shift segmentation method, this paper puts forward to an improved Mean shift segmentation method of high-resolution remote sensing image based on LBP and Canny features. The results show that this improved Mean shift segmentation access can enhance segmentation accuracy compared to the traditional Mean shift.

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