Remote Sensing Image Scene Classification Based on Global–Local Dual-Branch Structure Model

Xu, Kejie; Hong, Huixiao; Deng, Peifang

doi:10.1109/lgrs.2021.3075712

Cited by 39 publications

(31 citation statements)

References 32 publications

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“…In [37], a multilayer feature fusion network was developed with a data augmentation approach integrated into training to improve the generalization ability of the model. In [38], the authors proposed a global-local dual-branch structure (GLDBS) that allows a network to explore global and local discriminative features. In addition to CNN, Graph Convolutional Network (GCN)-based methods have also been explored.…”

Section: Remote Sensing Image Scene Classification Methodsmentioning

confidence: 99%

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

Xiao

Yun

et al. 2021

Applied Sciences

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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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Section: Remote Sensing Image Scene Classification Methodsmentioning

confidence: 99%

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

Xiao

Yun

et al. 2021

Applied Sciences

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“…To improve representational power, multi-branch methods employ multi-branch architecture to consider some different inputs such as multi-scale of an image [13], [14], or different images [15], [16]. Wang et al [17] proposed a multiscale representation by a global local dual-branch architecture.…”

Section: Cnn Cnnmentioning

confidence: 99%

Pairwise Comparison Network for Remote-Sensing Scene Classification

Zhang

Zheng²,

2022

IEEE Geosci. Remote Sensing Lett.

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Remote sensing scene classification aims to assign a specific semantic label to a remote sensing image. Recently, convolutional neural networks have greatly improved the performance of remote sensing scene classification. However, some confused images may be easily recognized as the incorrect category, which generally degrade the performance. The differences between image pairs can be used to distinguish image categories. This paper proposed a pairwise comparison network, which contains two main steps: pairwise selection and pairwise representation. The proposed network first selects similar image pairs, and then represents the image pairs with pairwise representations. The self-representation is introduced to highlight the informative parts of each image itself, while the mutualrepresentation is proposed to capture the subtle differences between image pairs. Comprehensive experimental results on two challenging datasets (AID, NWPU-RESISC45) demonstrate the effectiveness of the proposed network. The codes are provided in https://github.com/spectralpublic/PCNet.git.

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“…Hu et al [15] developed two schemes, named multi-S scale deep semantic representation and multi-level deep semantic representation, to generate semantic features and improve the scene classification performance. Xu et al [16] designed a remote sensing image classification method based on global-local dual-branch structure (GLDBS), which can learn discriminative features of the original images. Ma et al [17] proposed a CNN-based multilayer feature fusion method with weight adjustment, in which the feature fusion is realized by passing additional features to the same layer.…”

Section: Introductionmentioning

confidence: 99%

Relation-Attention Networks for Remote Sensing Scene Classification

Wang

Duan

Chen

et al. 2022

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

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Remote sensing (RS) scene classification plays an important role in a wide range of RS applications. Recently, convolutional neural networks (CNNs) have been applied to the field of scene classification in RS images and achieved impressive performance. However, to classify RS scenes, most of the existing CNN methods either utilize the high-level features from the last convolutional layer of CNNs, missing much important information existing at the other levels, or directly fuse the features at different levels, bringing redundant and/or mutually exclusive information. Inspired by the attention mechanism of the human visual system, in this paper, we explore a novel relation-attention model and design an end-to-end relationattention network (RANet) to learn powerful feature representations of multiple levels to further improve the classification performance. First, we propose to extract convolutional features at different levels by pre-trained CNNs. Second, a multi-scale feature computation module is constructed to connect features at different levels and generate multi-scale semantic features. Third, a novel relation-attention model is designed to focus on the critical features whilst avoiding the use of redundant and even distractive ones by exploiting the scale contextual information. Finally, the resulting relation-attention features are concatenated and fed into a softmax layer for the final classification. Experiments on four well-known RS scene classification data sets (UC-Merced, WHU-RS19, AID, and OPTIMAL-31) show that our method outperforms some state-ofthe-art algorithms. The code of our proposed method is publicly

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Remote Sensing Image Scene Classification Based on Global–Local Dual-Branch Structure Model

Cited by 39 publications

References 32 publications

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

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

Pairwise Comparison Network for Remote-Sensing Scene Classification

Relation-Attention Networks for Remote Sensing Scene Classification

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