Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint

Xie, Hao; Chen, Yushi; Ghamisi, Pedram

doi:10.3390/rs13132566

Cited by 18 publications

(12 citation statements)

References 47 publications

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“…Xie et al [11] applied label augmentation to process the data and a joint label was assigned to each generated image to consider data augmentation and label at the same time. Training set intra-class diversity was increased by augmented samples and applied for the classification process.…”

Section: Literature Surveymentioning

confidence: 99%

Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Vinaykumar¹,

Babu²,

Frnda

2023

EURASIP J. Adv. Signal Process.

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Satellite Image classification provides information about land use land cover (LULC) and this is required in many applications such as Urban planning and environmental monitoring. Recently, deep learning techniques were applied for satellite image classification and achieved higher efficiency. The existing techniques in satellite image classification have limitations of overfitting problems due to the convolutional neural network (CNN) model generating more features. This research proposes the optimal guidance-whale optimization algorithm (OG-WOA) technique to select the relevant features and reduce the overfitting problem. The optimal guidance technique increases the exploitation of the search technique by changing the position of the search agent related to the best fitness value. This increase in exploitation helps to select the relevant features and avoid overfitting problems. The input images are normalized and applied to AlexNet–ResNet50 model for feature extraction. The OG-WOA technique is applied in extracted features to select relevant features. Finally, the selected features are processed for classification using Bi-directional long short-term memory (Bi-LSTM). The proposed OG-WOA–Bi-LSTM technique has an accuracy of 97.12% on AID, 99.34% on UCM, and 96.73% on NWPU, SceneNet model has accuracy of 89.58% on AID, and 95.21 on the NWPU dataset.

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Section: Literature Surveymentioning

confidence: 99%

Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Vinaykumar¹,

Babu²,

Frnda

2023

EURASIP J. Adv. Signal Process.

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“…Rafael Pires et al [42] investigated the scene classification performance of CNN with transfer learning, which demonstrated the effectiveness of transfer learning from natural images to remote sensing images. Xie et al [43] developed a remote sensing image scene classification model with label augmentation, in which Kullback-Leibler divergence is utilized as the intra-class constraint to restrict the distribution of training data. Shi et al [44] proposed a lightweight CNN based on attention-oriented multi-branch feature fusion for remote sensing image scene classification.…”

Section: Remote Sensing Image Scene Classificationmentioning

confidence: 99%

Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

et al. 2021

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Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.

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“…R EMOTE sensing image scene classification [1]- [4] has been widely used in fields [5] such as land surveying, nature monitoring, and urban planning [6]. It has made great progress [7], [8] with the development of deep learning [9], [10] and automatic machine learning [11], such as neural architecture search (NAS) technology [12].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Scene Classification by Multiple Granularity Semantic Learning

Guo

Yang

et al. 2022

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

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Remote sensing image scene classification faces challenges such as the difference in semantic granularity of different scene categories and the imbalance of the number of samples, which cause the wrong features learning for deep convolutional networks (DCNs). This paper proposes a multiple granularity semantic learning network (MGSN), including multiple granularity semantic learning (MGSL) and nonuniform sampling augmentation (NUA) modules. Specifically, the MGSL module makes full use of different granularities of semantic information of scenes, guiding the network to learn global and local features simultaneously. And the relationship between semantic features of different granularity has been explored, based that the learning of coarse-grained features helps to improve the learning of fine-grained semantic features. It shows that learning fine-grain semantics can inhibit learning coarse-grain semantic features. The NUA module combines sampling and sample augmentation to balance the sample distribution, which can avoid overfitting caused by oversampling. The proposed MGSN achieved state-ofthe-art classification accuracy on two large-scale remote sensing image scene classification datasets, Million-AID and NWPU-RESISC45. Under 10% and 20% training samples of NWPU-RESISC45 dataset, MGSN achieves 91.92% and 94.33% top1 accuracy respectively. In experiments conducted on the Million-AID dataset, the proposed MGSN performed best among 18 DCNs. In comparison to the baseline, FixEfficientNet, MGSN improved the accuracy of top1 and top5 by 10.63% and 5.47%, respectively, with low complexity costs.

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Remote Sensing Image Scene Classification via Label Augmentation and Intra-Class Constraint

Cited by 18 publications

References 47 publications

Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Optimal guidance whale optimization algorithm and hybrid deep learning networks for land use land cover classification

Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification

Remote Sensing Image Scene Classification by Multiple Granularity Semantic Learning

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