Residual Dense Network Based on Channel-Spatial Attention for the Scene Classification of a High-Resolution Remote Sensing Image

The scene classification of a remote sensing image has been widely used in various fields as an important task of understanding the content of a remote sensing image. Specially, a high-resolution remote sensing scene contains rich information and complex content. Considering that the scene content in a remote sensing image is very tight to the spatial relationship characteristics, how to design an effective feature extraction network directly decides the quality of classification by fully mining the spatial information in a high-resolution remote sensing image. In recent years, convolutional neural networks (CNNs) have achieved excellent performance in remote sensing image classification, especially the residual dense network (RDN) as one of the representative networks of CNN, which shows a stronger feature learning ability as it fully utilizes all the convolutional layer information. Therefore, we design an RDN based on channel-spatial attention for scene classification of a high-resolution remote sensing image. First, multi-layer convolutional features are fused with residual dense blocks. Then, a channel-spatial attention module is added to obtain more effective feature representation. Finally, softmax classifier is applied to classify the scene after adopting data augmentation strategy for meeting the training requirements of the network parameters. Five experiments are conducted on the UC Merced Land-Use Dataset (UCM) and Aerial Image Dataset (AID), and the competitive results demonstrate that our method can extract more effective features and is more conducive to classifying a scene.

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Unsupervised feature learning for scene classification of high resolution remote sensing image

2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) ◽

10.1109/chinasip.2015.7230392 ◽

2015 ◽

Cited By ~ 2

Author(s):

Min Fu ◽

Yuan Yuan ◽

Xiaoqiang Lu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Feature Learning ◽

Remote Sensing Image ◽

Unsupervised Feature Learning ◽

Scene Classification

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A Combined Deep Learning Model for the Scene Classification of High-Resolution Remote Sensing Image

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2019.2902675 ◽

2019 ◽

Vol 16 (10) ◽

pp. 1540-1544 ◽

Cited By ~ 1

Author(s):

Yunya Dong ◽

Qian Zhang

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Remote Sensing Image ◽

Learning Model ◽

Scene Classification ◽

Deep Learning Model

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FEATURE FUSION FOR CROSS-MODAL SCENE CLASSIFICATION OF REMOTE SENSING IMAGE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-m-3-2021-63-2021 ◽

2021 ◽

Vol XLIV-M-3-2021 ◽

pp. 63-66

Author(s):

W. Geng ◽

W. Zhou ◽

S. Jin

Keyword(s):

Remote Sensing ◽

Feature Fusion ◽

Remote Sensing Image ◽

Aerial Images ◽

Aerial Image ◽

Svm Classifier ◽

Scene Classification ◽

Street View ◽

Modal Model

Abstract. Scene classification plays an important role in remote sensing field. Traditional approaches use high-resolution remote sensing images as data source to extract powerful features. Although these kind of methods are common, the model performance is severely affected by the image quality of the dataset, and the single modal (source) of images tend to cause the mission of some scene semantic information, which eventually degrade the classification accuracy. Nowadays, multi-modal remote sensing data become easy to obtain since the development of remote sensing technology. How to carry out scene classification of cross-modal data has become an interesting topic in the field. To solve the above problems, this paper proposes using feature fusion for cross-modal scene classification of remote sensing image, i.e., aerial and ground street view images, expecting to use the advantages of aerial images and ground street view data to complement each other. Our cross- modal model is based on Siamese Network. Specifically, we first train the cross-modal model by pairing different sources of data with aerial image and ground data. Then, the trained model is used to extract the deep features of the aerial and ground image pair, and the features of the two perspectives are fused to train a SVM classifier for scene classification. Our approach has been demonstrated using two public benchmark datasets, AiRound and CV-BrCT. The preliminary results show that the proposed method achieves state-of-the-art performance compared with the traditional methods, indicating that the information from ground data can contribute to aerial image classification.

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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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Multi-Task Joint Sparse and Low-Rank Representation for the Scene Classification of High-Resolution Remote Sensing Image

Remote Sensing ◽

10.3390/rs9010010 ◽

2016 ◽

Vol 9 (1) ◽

pp. 10 ◽

Cited By ~ 13

Author(s):

Kunlun Qi ◽

Wenxuan Liu ◽

Chao Yang ◽

Qingfeng Guan ◽

Huayi Wu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Remote Sensing Image ◽

Low Rank ◽

Scene Classification ◽

Low Rank Representation

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Semantic Multigranularity Feature Learning for High-Resolution Remote Sensing Image Scene Classification

Applied Sciences ◽

10.3390/app11199204 ◽

2021 ◽

Vol 11 (19) ◽

pp. 9204

Author(s):

Xinyi Ma ◽

Zhifeng Xiao ◽

Hong-sik Yun ◽

Seung-Jun Lee

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Spatial Information ◽

Feature Learning ◽

Remote Sensing Image ◽

Input Image ◽

Training Data ◽

Aerial Image ◽

Scene Classification ◽

Feature Maps

High-resolution remote sensing image scene classification is a challenging visual task due to the large intravariance and small intervariance between the categories. To accurately recognize the scene categories, it is essential to learn discriminative features from both global and local critical regions. Recent efforts focus on how to encourage the network to learn multigranularity features with the destruction of the spatial information on the input image at different scales, which leads to meaningless edges that are harmful to training. In this study, we propose a novel method named Semantic Multigranularity Feature Learning Network (SMGFL-Net) for remote sensing image scene classification. The core idea is to learn both global and multigranularity local features from rearranged intermediate feature maps, thus, eliminating the meaningless edges. These features are then fused for the final prediction. Our proposed framework is compared with a collection of state-of-the-art (SOTA) methods on two fine-grained remote sensing image scene datasets, including the NWPU-RESISC45 and Aerial Image Datasets (AID). We justify several design choices, including the branch granularities, fusion strategies, pooling operations, and necessity of feature map rearrangement through a comparative study. Moreover, the overall performance results show that SMGFL-Net consistently outperforms other peer methods in classification accuracy, and the superiority is more apparent with less training data, demonstrating the efficacy of feature learning of our approach.

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