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.

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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