High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network

Li, Fengpeng; Feng, Ruyi; Han, Wei; Wang, Lizhe

doi:10.1109/tgrs.2020.2987060

Cited by 79 publications

(36 citation statements)

References 72 publications

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“…Moreover, to tackle the interclass similarity issue and large intraclass variance issue, second-order information is efficiently applied in the RS scene classification task [32], [49], which receives excellent performance. More recently, Li et al [50] proposed a notable architecture KFBNet to extract more compact global features with the guidance of key local regions, which is now the SOTA method. In this article, we will mainly compare our results with [32], [49], and [50].…”

Section: A Remote Sensing Scene Classificationmentioning

confidence: 99%

“…More recently, Li et al [50] proposed a notable architecture KFBNet to extract more compact global features with the guidance of key local regions, which is now the SOTA method. In this article, we will mainly compare our results with [32], [49], and [50].…”

Section: A Remote Sensing Scene Classificationmentioning

confidence: 99%

“…Therefore, we also select it as baseline model. As for DenseNet121, the work [50] mainly uses it as baseline model. To make a fair comparison, we also choose it as another baseline model.…”

Section: B Implementation Detailsmentioning

confidence: 99%

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MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification

Zhao

Lyu

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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Remote sensing (RS) scene classification is a challenging task to predict scene categories of RS images. RS images have two main issues: large intraclass variance caused by large resolution variance and confusing information from large geographic covering area. To ease the negative influence from the above two issues. We propose a multigranularity multilevel feature ensemble network (MGML-FENet) to efficiently tackle the RS scene classification task in this article. Specifically, we propose multigranularity multilevel feature fusion branch (MGML-FFB) to extract multigranularity features in different levels of network by channel-separate feature generator (CS-FG). To avoid the interference from confusing information, we propose a multigranularity multilevel feature ensemble module (MGML-FEM), which can provide diverse predictions by full-channel feature generator (FC-FG). Compared to previous methods, our proposed networks have the ability to use structure information and abundant fine-grained features. Furthermore, through the ensemble learning method, our proposed MGML-FENets can obtain more convincing final predictions. Extensive classification experiments on multiple RS datasets (AID, NWPU-RESISC45, UC-Merced, and VGoogle) demonstrate that our proposed networks achieve better performance than previous state-of-the-art (SOTA) networks. The visualization analysis also shows the good interpretability of MGML-FENet.

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Section: A Remote Sensing Scene Classificationmentioning

confidence: 99%

Section: A Remote Sensing Scene Classificationmentioning

confidence: 99%

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MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification

Zhao

Lyu

et al. 2023

IEEE Trans. Neural Netw. Learning Syst.

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“…Thereafter, it was used in different applications, including computer vision [21] and RS image processing [22][23][24]. Accordingly, most of the studies reported an increase in the performance of the DL methods when guided with attention mechanism [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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“…In the early stage of development, traditional machine learning methods have been used for scene classification tasks, such as support vector machine and bag of words [2,3]. Recently, deep learning methods have been proven to be effective for extracting image features [4][5][6][7][8], and many studies have demonstrated effective scene classification performance with the help of deep learning from various novel perspectives including self-supervised learning [9], data augmentation [10], feature fusion [11][12][13][14][15], reconstructing networks [16][17][18][19][20][21][22][23], integration of spectral and spatial information [24], balancing global and local features, refining feature maps through encoding method [25], adding a new mechanism [26,27], as well as introducing a new network [28], open set problem [29], and noisy label distillation [30]. However, a lack of annotated data has restricted the development of deep learning methods in scene classification due to the high cost of annotating data.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

Sha

2021

Remote Sensing

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Unsupervised domain adaptation (UDA) based on adversarial learning for remote‐sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers according to their ability to distinguish features from source or target domains. However, they suffer from the following two limitations: (1) the classifier is trained on source samples and forms a source‐domain‐specificboundary, which ignores features from the target domain and (2) semantically meaningful features are merely built from the adversary of a generator and a discriminator, which ignore selecting the domain invariant features. These issues limit the distribution matching performance of source and target domains, since each domain has its distinctive characteristic. To resolve these issues, we propose a framework with error‐correcting boundaries and feature adaptation metric. Specifically, we design an error‐correcting boundaries mechanism to build target‐domain‐specific classifier boundaries via multi‐classifiers and error‐correcting discrepancy loss, which significantly distinguish target samples and reduce their distinguished uncertainty. Then, we employ a feature adaptation metric structure to enhance the adaptation of ambiguous features via shallow layers of the backbone convolutional neural network and alignment loss, which automatically learns domain invariant features. The experimental results on four public datasets outperform other UDA methods of remote‐sensing scene classification.

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High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network

Cited by 79 publications

References 72 publications

MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification

MGML: Multigranularity Multilevel Feature Ensemble Network for Remote Sensing Scene Classification

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Unsupervised Adversarial Domain Adaptation with Error-Correcting Boundaries and Feature Adaption Metric for Remote-Sensing Scene Classification

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