A New Method for Spatial Feature Extraction and Classification of Remote Sensing Image

2006 ◽  
Author(s):  
X. Zhang ◽  
S. Zhang ◽  
J. Xu ◽  
J. Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Remote Sensing Image ◽  
New Method ◽  
Spatial Feature

Object-Oriented Classification of Remote Sensing Image Based on SPM Feature Extraction

2011 ◽  
Vol 58-60 ◽  
pp. 1997-2001
Author(s):  
Xin Gang Li ◽  
Yan Hu
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Kernel Method ◽  
Mean Shift ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Basic Question ◽  
Support Vector ◽  
Pyramid Matching

The ultimate goal of remote sensing image processing is to analyze and interpret the image. The classification is the most basic question of remote sensing image information extraction. Object-oriented classification is proposed in recent years, whose image classification is based on image segmentation. This paper introduces the spatial pyramid matching kernel method (SPM) for feature extraction, the segmentation algorithm uses mean shift, and the classifier is support vector machines（SVM）. Taking a piece of land in southern California for example, we do two experiments, including our approach and a comparing test .Comparing the results, we can see that the object-oriented classification of remote sensing image which based on SPM feature extraction can greatly improve the accuracy.

scholarly journals A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification

2021 ◽  
Vol 13 (10) ◽  
pp. 1950
Author(s):  
Cuiping Shi ◽  
Xin Zhao ◽  
Liguo Wang
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Rapid Development ◽  
Remote Sensing Image ◽  
Classification Performance ◽  
Attention Mechanism ◽  
Scene Classification

In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.

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