An Adaptive Corner Detection Algorithm for Remote Sensing Image Based on Curvature Threshold

2009 ◽  
Author(s):  
Xiaolian Deng ◽  
Yuehua Huang ◽  
Shengqin Feng ◽  
Changyao Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Corner Detection

The Research of Remote Sensing Image Matching Based on the Improved Harris Corner Detection Algorithm

2011 ◽  
Vol 271-273 ◽  
pp. 201-204
Author(s):  
Yu Liu ◽  
Hong Chen ◽  
Yong Sheng Guo ◽  
Wen Bang Sun ◽  
Yao Yu Zhang
Keyword(s):  
Remote Sensing ◽  
Image Matching ◽  
Remote Sensing Image ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Good Stability ◽  
Exact Match ◽  
Harris Corner ◽  
Harris Corner Detection ◽  
Detection Speed

According to the lack of Harris corner detection algorithm in corner detection, positioning, detection speed and anti-noise ,this paper improves Harris operator to make up for the bad stability of original operator, so as to realize accurate inspection, Strong anti-noise, fast, good stability and the exact match of image.

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.

Sign in / Sign up

Export Citation Format

Share Document