Deep Discriminative Representation Learning with Attention Map for Scene Classification

Li, Jun; Lin, Daoyu; Wang, Yang; Xu, Guangluan; Zhang, Yunyan; Ding, Chibiao; Zhou, Yanhai

doi:10.3390/rs12091366

Cited by 70 publications

(51 citation statements)

References 62 publications

(84 reference statements)

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“…Beyond a traditional cross-entropy loss, a metric learning regularization term and a weight decay term are added to the proposed objective function. Li et al [22] constructed a feature fusion network that combining the original feature and attention map feature; besides that, they adopted center loss [37] to improve feature distinguishability.…”

Section: Cnn-based Methods Of Aerial Scene Classificationmentioning

confidence: 99%

“…The methods relying on deep neural networks automatically learn global features from the input data and cast the aerial scene classification task as an end-to-end problem. More recently, while the deep CNNs methods have become the new state-of-the-art solutions [19][20][21][22] for the aerial scene classification area, yet, there are clear limitations. Specifically, the most notorious drawback of deep learning methods is that they typically require vast quantities of labeled data and suffer from poor sample efficiency, which excludes many applications where data is intrinsically rare or expensive [23].…”

Section: Introductionmentioning

confidence: 99%

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Few-shot Classification of Aerial Scene Images via Meta-learning

Zhang¹,

Li²,

Wang³

et al. 2020

Preprint

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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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Section: Cnn-based Methods Of Aerial Scene Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Few-shot Classification of Aerial Scene Images via Meta-learning

Zhang¹,

Li²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The overall accuracy of BiMobileNet is 92.06% and 94.08% when the training ratios are 10% and 20%, respectively; this is higher than all but one other methods. When the training ratio is 10%, BiMobileNet accuracy is 2.1%,1.0% and 0.3% higher than SF-CNN [44], GLANet [46] and DML [49], respectively, and is similar to DDRL-AM [41]. SF-CNN, GLANet, and DML adopt deep CNN VGGNet; DDRL-AM adopts deep CNN ResNet18.…”

Section: Classification Of the Nwpu-resisc45 Datasetmentioning

confidence: 99%

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Guo

et al. 2020

Sensors

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Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

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“…Scene classification of RSI, i.e. automatically extracting valuable information from each scene image and categorizing them into different classes based on their semantic information, has become a research hotspot in RSI interpretation [1], [4], [5]. Scene classification of RSI has a wide range of applications, including urban planning, natural disaster detection, landcover/land-use classification, environment monitoring and so on [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network

2020

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Scene classification of high-resolution Remote Sensing Images (RSI) is one of basic challenges in RSI interpretation. Existing scene classification methods based on deep learning have achieved impressive performances. However, since RSI commonly contain various types of ground objects and complex backgrounds, most of methods cannot focus on saliency features of scene, which limits the classification performances. To address this issue, we propose a novel Saliency Dual Attention Residual Network (SDAResNet) to extract both cross-channel and spatial saliency information for scene classification of RSI. More specifically, the proposed SDAResNet consists of spatial attention and channel attention, in which spatial attention is embedded in low-level feature to emphasize saliency location information and suppress background information, and channel attention is integrated to high-level features to extract saliency meaningful information. Additionally, several image classification tricks are used to further improve classification accuracy. Finally, Extensive experiments on two challenging benchmark RSI datasets are presented to demonstrate that our methods outperform most of state-of-the-art approaches significantly.

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Deep Discriminative Representation Learning with Attention Map for Scene Classification

Cited by 70 publications

References 62 publications

Few-shot Classification of Aerial Scene Images via Meta-learning

Few-shot Classification of Aerial Scene Images via Meta-learning

An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network

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