scholarly journals Remote sensing image classification with GIS data based on spatial data mining techniques

2000 ◽  
Vol 3 (4) ◽  
pp. 30-35 ◽  
Author(s):  
Di Kaichang ◽  
Li Deren ◽  
Li Deyi

Author(s):  
Z. Yan ◽  
Y. Yang

Image processing has been one of the efficient technologies for GIS data requisition. Support Vector Machines (SVMs) have peculiar advantages in handling problems with small sample sizes, nonlinearity, and high dimensionality. However, SVMs can only solve two-class problems while multi-class decision is impossible. Error correcting output coding (ECOC) SVMs enhance the ability of fault tolerance when solving multi-class classification problems, which makes ECOC SVMs suitable for remote sensing image classification. In this paper, the generalization ability of ECOC SVMs is discussed. ECOC SVMs with optimum coding matrices are selected by experiment, and applied to remote sensing image classification. Experimental results show that, compared with Conventional multi-class classification methods, less SVM sub-classifiers are needed for ECOC SVMs in remote sensing image classification, and the classification accuracy is also improved.



Author(s):  
Sumit Kaur

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. 





2019 ◽  
Vol 16 (7) ◽  
pp. 1150-1154 ◽  
Author(s):  
Xiang-Jun Shen ◽  
Xiao-Zhen Luo ◽  
Timothy Apasiba Abeo ◽  
Yang Yang ◽  
Xi Shao ◽  
...  


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