Linear Features Extraction From Remote Sensing Image Based on Wedgelet Decomposition

2007 ◽  
Author(s):  
Ruiqing Niu ◽  
Xiaoming Mei ◽  
Liang-pei Zhang ◽  
Ping-xiang Li
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Features Extraction ◽  
Linear Features

Linear features adaptive extraction from remote sensing image based on beamlet transform

2006 ◽  
Author(s):  
Xiaoming Mei ◽  
Ruiqing Niu ◽  
Liang-pei Zhang ◽  
Ping-xiang Li
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Linear Features ◽  
Beamlet Transform

Research on Linear Features Change Detection Based on Remote Sensing Image and Vector Data

2013 ◽  
Vol 679 ◽  
pp. 83-87
Author(s):  
Zuo Chang Zhang ◽  
Xin Peng ◽  
Tian Qi
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Template Matching ◽  
Detection Method ◽  
Buffer Zone ◽  
Remote Sensing Image ◽  
Vector Data ◽  
Linear Features ◽  
Multi Scale ◽  
Edge Points

To prompt the present situation and utilized values of fundamental geo-information, this paper focuses on a change detection method based on remote sensing image and GIS vector for linear features. Firstly unilateral vector was taken as original value of linear features; then edge points were picked up by pyramid decomposition and multi-scale template matching, and Ziplock Snake method was adopted to further improve the extraction results; finally buffer zone was constructed to distinguish the changed part. This change detection method proves to have higher degree of automation and more precise, so long as the registration of remote sensing image and vector map is accurate.

scholarly journals Multi-Level Features Extraction for Discontinuous Target Tracking in Remote Sensing Image Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4855 ◽  
Author(s):  
Bin Zhou ◽  
Xuemei Duan ◽  
Dongjun Ye ◽  
Wei Wei ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Target Tracking ◽  
Principal Component ◽  
Remote Sensing Image ◽  
Features Extraction ◽  
Reference Image ◽  
Final Decision ◽  
Image Monitoring ◽  
Multi Level ◽  
Adaptive Neighborhood

Many techniques have been developed for computer vision in the past years. Features extraction and matching are the basis of many high-level applications. In this paper, we propose a multi-level features extraction for discontinuous target tracking in remote sensing image monitoring. The features of the reference image are pre-extracted at different levels. The first-level features are used to roughly check the candidate targets and other levels are used for refined matching. With Gaussian weight function introduced, the support of matching features is accumulated to make a final decision. Adaptive neighborhood and principal component analysis are used to improve the description of the feature. Experimental results verify the efficiency and accuracy of the proposed method.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

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