Paper Info
Title
Deep Hierarchical Representation And Segmentation Of High Resolution Remote Sensing Images
Abstract
This paper presents a novel deep hierarchical representation and segmentation approach for high resolution remote sensing image understanding. An information extraction approach using deep hierarchical exploitation for remote sensing image is presented. The key idea is that we adopt a fast scanning image segmentation within a deep hierarchical feature representation framework, using a deep learning technique to split and merge over-segmented regions until they form meaningful objects. The contribution is to develop an effective procedure for multi-scale image representation to address the issue of information uncertainty in practical applications. We test our method on two optical high resolution remote sensing image datasets and produce promising experimental results in the form of multiple layer outputs, which confirm the effectiveness and robustness of the proposed procedure.
Year
Venue
Keywords
2015
2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Hierarchical representation, Image segmentation, High resolution remote sensing images
Field
DocType
ISSN
Scale-space segmentation,Feature detection (computer vision),Image fusion,Computer science,Remote sensing,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Computer vision,Pattern recognition,Image texture,Segmentation,Image resolution
Conference
2153-6996
Citations 
PageRank 
References 
0
0.34
4
Authors
7
Name
Order
Citations
PageRank
Jun Wang1135.63
Qi-ming Qin215849.12
Zhoujing Li301.01
Xin Ye4258.36
Jianhua Wang532.77
Xiucheng Yang6327.04
Xuebin Qin7192.23