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
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Image Classification ◽  
Spatial Data ◽  
Remote Sensing Image ◽  
Spatial Data Mining ◽  
Data Mining Techniques ◽  
Remote Sensing Image Classification ◽  
Gis Data

scholarly journals Application of ECOC SVMS in Remote Sensing Image Classification

2014 ◽  
Vol XL-2 ◽  
pp. 191-196 ◽  
Author(s):  
Z. Yan ◽  
Y. Yang
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Small Sample ◽  
Support Vector ◽  
Classification Problems ◽  
Remote Sensing Image Classification ◽  
Gis Data ◽  
Multi Class Classification ◽  
Small Sample Sizes

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.

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. 

scholarly journals Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 44974-44988
Author(s):  
Na Liu ◽  
Bin Zhang ◽  
Qiuhuan Ma ◽  
Qingqing Zhu ◽  
Xiaoling Liu
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Image Classification ◽  
Hyperspectral Remote Sensing Image

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
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