Feature extraction and scene classification for remote sensing image based on sparse representation

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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A Convolutional Neural Network Based on Grouping Structure for Scene Classification

Remote Sensing ◽

10.3390/rs13132457 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2457

Author(s):

Xuan Wu ◽

Zhijie Zhang ◽

Wanchang Zhang ◽

Yaning Yi ◽

Chuanrong Zhang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Feature Extraction ◽

Convolutional Neural Network ◽

Data Augmentation ◽

Remote Sensing Image ◽

Difficult Problem ◽

Image Features ◽

Attention Mechanism ◽

Scene Classification

Convolutional neural network (CNN) is capable of automatically extracting image features and has been widely used in remote sensing image classifications. Feature extraction is an important and difficult problem in current research. In this paper, data augmentation for avoiding over fitting was attempted to enrich features of samples to improve the performance of a newly proposed convolutional neural network with UC-Merced and RSI-CB datasets for remotely sensed scene classifications. A multiple grouped convolutional neural network (MGCNN) for self-learning that is capable of promoting the efficiency of CNN was proposed, and the method of grouping multiple convolutional layers capable of being applied elsewhere as a plug-in model was developed. Meanwhile, a hyper-parameter C in MGCNN is introduced to probe into the influence of different grouping strategies for feature extraction. Experiments on the two selected datasets, the RSI-CB dataset and UC-Merced dataset, were carried out to verify the effectiveness of this newly proposed convolutional neural network, the accuracy obtained by MGCNN was 2% higher than the ResNet-50. An algorithm of attention mechanism was thus adopted and incorporated into grouping processes and a multiple grouped attention convolutional neural network (MGCNN-A) was therefore constructed to enhance the generalization capability of MGCNN. The additional experiments indicate that the incorporation of the attention mechanism to MGCNN slightly improved the accuracy of scene classification, but the robustness of the proposed network was enhanced considerably in remote sensing image classifications.

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Scene Classification of High-Resolution Remote Sensing Image Using Transfer Learning with Multi-model Feature Extraction Framework

Image and Graphics Technologies and Applications - Communications in Computer and Information Science ◽

10.1007/978-981-13-1702-6_24 ◽

2018 ◽

pp. 238-251 ◽

Cited By ~ 1

Author(s):

Guandong Li ◽

Chunju Zhang ◽

Mingkai Wang ◽

Fei Gao ◽

Xueying Zhang

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

High Resolution ◽

Transfer Learning ◽

Remote Sensing Image ◽

Scene Classification

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An End-to-End Local-Global-Fusion Feature Extraction Network for Remote Sensing Image Scene Classification

Remote Sensing ◽

10.3390/rs11243006 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3006 ◽

Cited By ~ 4

Author(s):

Yafei Lv ◽

Xiaohan Zhang ◽

Wei Xiong ◽

Yaqi Cui ◽

Mi Cai

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Feature Fusion ◽

Remote Sensing Image ◽

Local Features ◽

Feature Representation ◽

Scene Classification ◽

Global Features ◽

End To End ◽

Fusion Feature

Remote sensing image scene classification (RSISC) is an active task in the remote sensing community and has attracted great attention due to its wide applications. Recently, the deep convolutional neural networks (CNNs)-based methods have witnessed a remarkable breakthrough in performance of remote sensing image scene classification. However, the problem that the feature representation is not discriminative enough still exists, which is mainly caused by the characteristic of inter-class similarity and intra-class diversity. In this paper, we propose an efficient end-to-end local-global-fusion feature extraction (LGFFE) network for a more discriminative feature representation. Specifically, global and local features are extracted from channel and spatial dimensions respectively, based on a high-level feature map from deep CNNs. For the local features, a novel recurrent neural network (RNN)-based attention module is first proposed to capture the spatial layout information and context information across different regions. Gated recurrent units (GRUs) is then exploited to generate the important weight of each region by taking a sequence of features from image patches as input. A reweighed regional feature representation can be obtained by focusing on the key region. Then, the final feature representation can be acquired by fusing the local and global features. The whole process of feature extraction and feature fusion can be trained in an end-to-end manner. Finally, extensive experiments have been conducted on four public and widely used datasets and experimental results show that our method LGFFE outperforms baseline methods and achieves state-of-the-art results.

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