SEMSDNet: A Multi-Scale Dense Network with Attention for Remote Sensing Scene Classification

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

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Multi-Scale Convolutional Neural Network for Remote Sensing Scene Classification

2018 IEEE International Conference on Electro/Information Technology (EIT) ◽

10.1109/eit.2018.8500107 ◽

2018 ◽

Cited By ~ 5

Author(s):

Haikel Alhichri ◽

Naif Alajlan ◽

Yakoub Bazi ◽

Timon Rabczuk

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Convolutional Neural Network ◽

Scene Classification ◽

Multi Scale

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Multi-Scale Meta-Learning-Based Networks for High-Resolution Remote Sensing Scene Classification

10.1109/igarss47720.2021.9555134 ◽

2021 ◽

Author(s):

Xu Tang ◽

Weiquan Lin ◽

Chao Liu ◽

Xiao Han ◽

Wenjing Wang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Scene Classification ◽

Multi Scale ◽

Meta Learning

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A Multi-Scale Feature Aggregation Network Based on Channel-Spatial Attention for Remote Sensing Scene Classification

10.1109/igarss47720.2021.9554855 ◽

2021 ◽

Author(s):

Ming Li ◽

Lin Lei ◽

Xiao Li ◽

Yuli Sun

Keyword(s):

Remote Sensing ◽

Spatial Attention ◽

Scene Classification ◽

Scale Feature ◽

Multi Scale ◽

Feature Aggregation

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

Remote Sensing ◽

10.3390/rs12111887 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1887 ◽

Cited By ~ 5

Author(s):

Xiaolei Zhao ◽

Jing Zhang ◽

Jimiao Tian ◽

Li Zhuo ◽

Jie Zhang

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Spatial Attention ◽

Remote Sensing Image ◽

Learning Ability ◽

Aerial Image ◽

Dense Network ◽

Scene Classification ◽

Training Requirements

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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A Multi-Scale Approach for Remote Sensing Scene Classification Based on Feature Maps Selection and Region Representation

Remote Sensing ◽

10.3390/rs11212504 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2504 ◽

Cited By ~ 3

Author(s):

Jun Zhang ◽

Min Zhang ◽

Lukui Shi ◽

Wenjie Yan ◽

Bin Pan

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Remote Sensing Image ◽

Feature Representation ◽

Features Selection ◽

Scene Classification ◽

Feature Maps ◽

Global Features ◽

Convolutional Networks ◽

Multi Scale

Scene classification is one of the bases for automatic remote sensing image interpretation. Recently, deep convolutional neural networks have presented promising performance in high-resolution remote sensing scene classification research. In general, most researchers directly use raw deep features extracted from the convolutional networks to classify scenes. However, this strategy only considers single scale features, which cannot describe both the local and global features of images. In fact, the dissimilarity of scene targets in the same category may result in convolutional features being unable to classify them into the same category. Besides, the similarity of the global features in different categories may also lead to failure of fully connected layer features to distinguish them. To address these issues, we propose a scene classification method based on multi-scale deep feature representation (MDFR), which mainly includes two contributions: (1) region-based features selection and representation; and (2) multi-scale features fusion. Initially, the proposed method filters the multi-scale deep features extracted from pre-trained convolutional networks. Subsequently, these features are fused via two efficient fusion methods. Our method utilizes the complementarity between local features and global features by effectively exploiting the features of different scales and discarding the redundant information in features. Experimental results on three benchmark high-resolution remote sensing image datasets indicate that the proposed method is comparable to some state-of-the-art algorithms.

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