A Novel Deep Nearest Neighbor Neural Network for Few-Shot Remote Sensing Image Scene Classification

Chen, Yanqiao; Li, Yangyang; Mao, Heting; Chai, Xinghua; Jiao, Licheng

doi:10.3390/rs15030666

Cited by 9 publications

(4 citation statements)

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“…Few-shot learning aims to utilize a very small number of training samples to achieve effective training and fast learning. Chen [94] and others worked on how to improve the accuracy of scene classification in the presence of sample scarcity. To this end, they proposed a few-shot classification method called DN4AM.…”

Section: Few-shot Learningmentioning

confidence: 99%

Land Use and Land Cover Classification Meets Deep Learning: A Review

Zhao,

Tu,

et al. 2023

Sensors

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As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth’s surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.

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Section: Few-shot Learningmentioning

confidence: 99%

Land Use and Land Cover Classification Meets Deep Learning: A Review

Zhao,

Tu,

et al. 2023

Sensors

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“…We conducted experiments to address the 5-way 1-shot and 5-way 5-shot tasks on NWPU-RESISC45, UC Merced, and WHU-RS19 datasets. To compare the performance, we evaluated our method against five renowned few-shot learning techniques: Match-ingNet [35], RelationNet [48], MAML [49], Meta-SGD [50], DLA-MatchNet [18], DN4 [44] and DN4AM [36]. Given that our approach is based on the DN4AM architecture, we compared it to DN4AM using the same embedding network to ensure a fair comparison.…”

Section: Experimental Designmentioning

confidence: 99%

“…Given that our approach is based on the DN4AM architecture, we compared it to DN4AM using the same embedding network to ensure a fair comparison. The classification results are assessed using the average accuracy of the top-1, and the 95% confidence intervals (CI) [36] are provided. The input image is reduced to a size of 224 × 224 in a stochastic way, and enhanced with the common image enhancement methods.…”

Section: Experimental Designmentioning

confidence: 99%

“…For this reason, the acquisition of a classifier capable of extracting discriminative features from RSI significantly contributes to enhancing the performance of RSISC. To solve the few-shot RSISC, Chen et al [36] proposed the deep nearest neighbor neural network based on attention mechanism (DN4AM). DN4AM employs an episodic training technique for network training and performs evaluations on novel classes to enhance few-shot learning.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Discriminative Enhancement Method for Few-Shot Remote Sensing Image Scene Classification

Chen,

Li,

Mao

et al. 2023

Remote Sensing

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Remote sensing image scene classification (RSISC) has garnered significant attention in recent years. Numerous methods have been put forward in an attempt to tackle this issue, particularly leveraging deep learning methods that have shown promising performance in classifying remote sensing image (RSI). However, it is widely recognized that deep learning methods typically require a substantial amount of labeled data to effectively converge. Acquiring a sufficient quantity of labeled data often necessitates significant human and material resources. Hence, few-shot RSISC has become highly meaningful. Fortunately, the recently proposed deep nearest neighbor neural network based on the attention mechanism (DN4AM) model incorporates episodic training and class-related attention mechanisms, effectively reducing the impact of background noise regions on classification results. Nevertheless, the DN4AM model does not address the problem of significant intra-class variability and substantial inter-class similarities observed in RSI scenes. Therefore, the discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) is proposed to address the few-shot RSISC task. Our method makes three contributions. Firstly, we introduce center loss to enhance the intra-class feature compactness. Secondly, we utilize the deep local-global descriptor (DLGD) to increase inter-class feature differentiation. Lastly, we modify the Softmax loss by incorporating cosine margin to amplify the inter-class feature dissimilarity. Experiments are conducted on three diverse RSI datasets to gauge the efficacy of our approach. Through comparative analysis with various cutting-edge methods including MatchingNet, RelationNet, MAML, Meta-SGD, DN4, and DN4AM, our approach showcases promising outcomes in the few-shot RSISC task.

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