scholarly journals Supervised and Semi-Supervised Multi-View Canonical Correlation Analysis Ensemble for Heterogeneous Domain Adaptation in Remote Sensing Image Classification

2017 ◽  
Vol 9 (4) ◽  
pp. 337 ◽  
Author(s):  
Alim Samat ◽  
Claudio Persello ◽  
Paolo Gamba ◽  
Sicong Liu ◽  
Jilili Abuduwaili ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Correlation Analysis ◽  
Image Classification ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Domain Adaptation ◽  
Remote Sensing Image ◽  
Remote Sensing Image Classification

Laplacian Regularized Kernel Canonical Correlation Ensemble for Remote Sensing Image Classification

2019 ◽  
Vol 16 (7) ◽  
pp. 1150-1154 ◽  
Author(s):  
Xiang-Jun Shen ◽  
Xiao-Zhen Luo ◽  
Timothy Apasiba Abeo ◽  
Yang Yang ◽  
Xi Shao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Canonical Correlation ◽  
Remote Sensing Image ◽  
Remote Sensing Image Classification

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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