A Lightweight and Discriminative Model for Remote Sensing Scene Classification With Multidilation Pooling Module

Zhang, Bin; Zhang, Yongjun; Wang, Shugen

doi:10.1109/jstars.2019.2919317

Cited by 96 publications

(74 citation statements)

References 65 publications

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“…Sun et al [38] adopted the gated bidirectional connection method for feature fusion. Zhang et al [39] used a multi-dilation pooling module, inverse residual, and channel attention in CNN, and designed a lightweight scene classification model.…”

Section: A Scene Classification Of Remote Sensing Images Using Cnnsmentioning

confidence: 99%

Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification

Guo

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Remote sensing image scene classification is an important means for the understanding of remote sensing images. Convolutional neural networks have been successfully applied to remote sensing image scene classification and have demonstrated remarkable performance. However, with improvements in image resolution, remote sensing image categories are becoming increasingly diverse, and problems such as high intraclass diversity and high interclass similarity have arisen. The performance of ordinary convolutional neural networks at distinguishing increasingly complex remote sensing images is still limited. Therefore, we propose a feature fusion framework based on hierarchical attention and bilinear pooling called HABFNet for the scene classification of remote sensing images. First, the deep convolutional neural network ResNet50 is used to extract the deep features from different layers of the image, and these features are fused to boost their robustness and effectiveness. Second, we design an improved channel attention scheme to enhance the features from different layers. Finally, the enhanced features are cross-layer bilinearly pooled and fused, and the fused features are used for classification. Extensive experiments were conducted on three publicly available remote sensing image benchmarks. Comparisons with the state-of-the-art methods demonstrated that the proposed HABFNet achieved competitive classification performance.

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Section: A Scene Classification Of Remote Sensing Images Using Cnnsmentioning

confidence: 99%

Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification

Guo

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Due to the addition of handcrafted features, the classification pipeline is also divided into two stages, which cannot be trained or implemented in an end-to-end manner. There are still few methods available for remote sensing image scene classification and object detection (such as methods proposed by Zhang et al [60], Zhang et al [61] Teimouri et al [62], etc.,) so far.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Guo

et al. 2020

Sensors

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Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

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“…In this study, we focus on the problem of remote sensing image scene classification based on deep learning, which has been widely used in urban planning, disaster monitoring, and other fields, to provide a high-level interpretation ability for high-resolution remote sensing images. It has become an important research topic and has received extensive attention from researchers [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…In the NC scenario, λ 1 = 0, λ 2 = 2 3 , λ 3 =3 4 , λ 4 =4 5 , and λ 5 = 5 6 . In the NIC scenario, λ 1 = 0, λ2 = 2 3 , λ 3 = 3 4 , • • • , and λ 15 = 15 16 .…”

mentioning

confidence: 99%

CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification

Jiang

et al. 2020

Sensors

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Remote sensing image scene classification has a high application value in the agricultural, military, as well as other fields. A large amount of remote sensing data is obtained every day. After learning the new batch data, scene classification algorithms based on deep learning face the problem of catastrophic forgetting, that is, they cannot maintain the performance of the old batch data. Therefore, it has become more and more important to ensure that the scene classification model has the ability of continual learning, that is, to learn new batch data without forgetting the performance of the old batch data. However, the existing remote sensing image scene classification datasets all use static benchmarks and lack the standard to divide the datasets into a number of sequential learning training batches, which largely limits the development of continual learning in remote sensing image scene classification. First, this study gives the criteria for training batches that have been partitioned into three continual learning scenarios, and proposes a large-scale remote sensing image scene classification database called the Continual Learning Benchmark for Remote Sensing (CLRS). The goal of CLRS is to help develop state-of-the-art continual learning algorithms in the field of remote sensing image scene classification. In addition, in this paper, a new method of constructing a large-scale remote sensing image classification database based on the target detection pretrained model is proposed, which can effectively reduce manual annotations. Finally, several mainstream continual learning methods are tested and analyzed under three continual learning scenarios, and the results can be used as a baseline for future work.

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A Lightweight and Discriminative Model for Remote Sensing Scene Classification With Multidilation Pooling Module

Cited by 96 publications

References 65 publications

Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification

Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification

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