A Meta-Learning Framework for Few-Shot Classification of Remote Sensing Scene

CNN-based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method outperforms three state-of-the-art few-shot algorithms and one typical CNN-based method, D-CNN. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale and support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

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Meta-FSEO: A Meta-Learning Fast Adaptation with Self-Supervised Embedding Optimization for Few-Shot Remote Sensing Scene Classification

Remote Sensing ◽

10.3390/rs13142776 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2776

Author(s):

Yong Li ◽

Zhenfeng Shao ◽

Xiao Huang ◽

Bowen Cai ◽

Song Peng

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Remote Sensing Data ◽

Scene Classification ◽

Generalization Capability ◽

Urban Scenes ◽

Shot Classification ◽

Meta Learning ◽

Land Surfaces ◽

Fast Adaptation

The performance of deep learning is heavily influenced by the size of the learning samples, whose labeling process is time consuming and laborious. Deep learning algorithms typically assume that the training and prediction data are independent and uniformly distributed, which is rarely the case given the attributes and properties of different data sources. In remote sensing images, representations of urban land surfaces can vary across regions and by season, demanding rapid generalization of these surfaces in remote sensing data. In this study, we propose Meta-FSEO, a novel model for improving the performance of few-shot remote sensing scene classification in varying urban scenes. The proposed Meta-FSEO model deploys self-supervised embedding optimization for adaptive generalization in new tasks such as classifying features in new urban regions that have never been encountered during the training phase, thus balancing the requirements for feature classification tasks between multiple images collected at different times and places. We also created a loss function by weighting the contrast losses and cross-entropy losses. The proposed Meta-FSEO demonstrates a great generalization capability in remote sensing scene classification among different cities. In a five-way one-shot classification experiment with the Sentinel-1/2 Multi-Spectral (SEN12MS) dataset, the accuracy reached 63.08%. In a five-way five-shot experiment on the same dataset, the accuracy reached 74.29%. These results indicated that the proposed Meta-FSEO model outperformed both the transfer learning-based algorithm and two popular meta-learning-based methods, i.e., MAML and Meta-SGD.

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

Remote Sensing ◽

10.3390/rs13142728 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2728

Author(s):

Qingjie Zeng ◽

Jie Geng ◽

Kai Huang ◽

Wen Jiang ◽

Jun Guo

Keyword(s):

Remote Sensing ◽

Remote Sensing Image ◽

Classification Performance ◽

Scene Classification ◽

Remote Sensing Images ◽

Training Set ◽

Shot Classification ◽

Support Set ◽

Feature Expression

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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RS-SSKD: Self-Supervision Equipped with Knowledge Distillation for Few-Shot Remote Sensing Scene Classification

Sensors ◽

10.3390/s21051566 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1566

Author(s):

Pei Zhang ◽

Ying Li ◽

Dong Wang ◽

Jiyue Wang

Keyword(s):

Remote Sensing ◽

State Of The Art ◽

Ground Truth ◽

Training Data ◽

Scene Classification ◽

Training Time ◽

Shot Classification ◽

Meta Learning ◽

Knowledge Distillation ◽

Few Data

While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.

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Few-Shot Classification of Aerial Scene Images via Meta-Learning

Remote Sensing ◽

10.3390/rs13010108 ◽

2020 ◽

Vol 13 (1) ◽

pp. 108

Author(s):

Pei Zhang ◽

Yunpeng Bai ◽

Dong Wang ◽

Bendu Bai ◽

Ying Li

Keyword(s):

Classification Problem ◽

Training Data ◽

Scene Classification ◽

Shot Classification ◽

Training Stage ◽

Testing Stage ◽

Meta Learning ◽

Cosine Distance ◽

The Impact

Convolutional neural network (CNN) based methods have dominated the field of aerial scene classification for the past few years. While achieving remarkable success, CNN-based methods suffer from excessive parameters and notoriously rely on large amounts of training data. In this work, we introduce few-shot learning to the aerial scene classification problem. Few-shot learning aims to learn a model on base-set that can quickly adapt to unseen categories in novel-set, using only a few labeled samples. To this end, we proposed a meta-learning method for few-shot classification of aerial scene images. First, we train a feature extractor on all base categories to learn a representation of inputs. Then in the meta-training stage, the classifier is optimized in the metric space by cosine distance with a learnable scale parameter. At last, in the meta-testing stage, the query sample in the unseen category is predicted by the adapted classifier given a few support samples. We conduct extensive experiments on two challenging datasets: NWPU-RESISC45 and RSD46-WHU. The experimental results show that our method yields state-of-the-art performance. Furthermore, several ablation experiments are conducted to investigate the effects of dataset scale, the impact of different metrics and the number of support shots; the experiment results confirm that our model is specifically effective in few-shot settings.

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A DEEP LEARNING FRAMEWORK FOR CLASSIFICATION OF SEVERITY IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE (COPD)

10.26226/morressier.5ade45fed462b8029238e7b4 ◽

2018 ◽

Author(s):

Roger Tam

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Deep Learning ◽

Pulmonary Disease ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Learning Framework

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Actual issues of systematization of scientific knowledge on the conditions of shot in the forensic ballistics

Vestnik of the St. Petersburg University of the Ministry of Internal Affairs of Russia ◽

10.35750/2071-8284-2020-3-164-169 ◽

2020 ◽

Vol 2020 (3) ◽

pp. 164-169

Author(s):

Igor' Latyshov ◽

Fedor Samuylenko

Keyword(s):

Scientific Knowledge ◽

Communication Systems ◽

Production Method ◽

Natural Phenomenon ◽

Projectile Energy ◽

Energy Characteristics ◽

Shot Classification ◽

Forensic Ballistics

In this research, there was considered a challenge of constructing a system of scientific knowledge of the shot conditions in judicial ballistics. It was observed that there are underlying factors that are intended to ensureits [scientific knowledge] consistency: identification of the list of shot conditions, which require consideration when solving expert-level research tasks on weapons, cartridges and traces of their action; determination of the communication systems in the course of objects’ interaction, which present the result of exposure to the conditions of the shot; classification of the shot conditions based on the grounds significant for solving scientific and practical problems. The article contains the characteristics of a constructive, functional factor (condition) of weapons and cartridges influence, environmental and fire factors, the structure of the target and its physical properties, situational and spatial factors, and projectile energy characteristics. Highlighted are the forms of connections formed in the course of objects’ interaction, proposed are the author’s classifications of forensically significant shooting conditions with them being divided on the basis of the following criteria: production from the object of interaction, production from a natural phenomenon, production method, results weapon operation and utilization, duration of exposure, type of structural connections between interaction objects, number of conditions that apply when firing and the forming traces.

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