Convolutional Attention in Ensemble With Knowledge Transferred for Remote Sensing Image Classification

Wang, Hainan; Miao, Yunqi; Wang, Hongren; Zhang, Baochang

doi:10.1109/lgrs.2018.2878350

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…Wang et al [48] constructed a multi-instance pooling module based on channel attention to transform the extracted features into instance vectors to highlight the local information associated with the scene labels. Wang et al [49] combined an attention mechanism with ensemble learning and proposed a method named convolutional attention in ensemble (CAE). Their method utilizes the proposed module to convert the weights in a basic classifier to the information required by the final classifier.…”

Section: Attention Mechanismsmentioning

confidence: 99%

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A lightweight and stochastic depth residual attention network for remote sensing scene classification

Wang

Yuan

et al. 2023

IET Image Processing

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Due to the rapid development of satellite technology, high‐spatial‐resolution remote sensing (HRRS) images have highly complex spatial distributions and multiscale features, making the classification of such images a challenging task. The key to scene classification is to accurately understand the main semantic information contained in images. Convolutional neural networks (CNNs) have outstanding advantages in this field. Deep CNNs (D‐CNNs) with better performance tend to have more parameters and higher complexity. However, shallow CNNs have difficulty extracting the key features of complex remote sensing images. In this paper, we propose a lightweight network with a random depth strategy for remote sensing scene classification (LRSCM). We construct a convolutional feature extraction module, DCAB, which incorporates depthwise separable convolutional and inverted residual structures, effectively reducing the numbers of required parameters and computations, and retains and utilizes low‐level features. In addition, coordinate attention (CA) is integrated into the module, thereby further improving the network's ability to extract key local information. To further reduce the complexity of model training, the residual module adopts a stochastic depth strategy, providing the network with a random depth. Comparative experiments on five public datasets show that the LRSCM network can achieve results comparable to those of other state‐of‐the‐art methods.

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Section: Attention Mechanismsmentioning

confidence: 99%

“…Wang et al. [49] combined an attention mechanism with ensemble learning and proposed a method named convolutional attention in ensemble (CAE). Their method utilizes the proposed module to convert the weights in a basic classifier to the information required by the final classifier.…”

Section: Related Workmentioning

confidence: 99%

A lightweight and stochastic depth residual attention network for remote sensing scene classification

Wang

Yuan

et al. 2023

IET Image Processing

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“…The authors in [41] introduces attention-based weighting scheme into ensemble learning. Their method called convolutional attention in ensemble (CAE), transfers the knowledge contained in base classifiers into the final classifier using convolutional attention models.…”

Section: Figurementioning

confidence: 99%

Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

et al. 2021

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Scene classification is a highly useful task in Remote Sensing (RS) applications. Many efforts have been made to improve the accuracy of RS scene classification. Scene classification is a challenging problem, especially for large datasets with tens of thousands of images with a large number of classes and taken under different circumstances. One problem that is observed in scene classification is the fact that for a given scene, only one part of it indicates which class it belongs to, whereas the other parts are either irrelevant or they actually tend to belong to another class. To address this issue, this paper proposes a deep attention Convolutional Neural Network (CNN) for scene classification in remote sensing. CNN models use successive convolutional layers to learn feature maps from larger and larger regions (or receptive fields) of the scene. The attention mechanism computes a new feature map as a weighted average of these original feature maps. In particular, we propose a solution, named EfficientNet-B3-Attn-2, based on the pre-trained EfficientNet-B3 CNN enhanced with an attention mechanism. A dedicated branch is added to layer 262 of the network, to compute the required weights. These weights are learned automatically by training the whole CNN model end-to-end using the backpropagation algorithm. In this way, the network learns to emphasize important regions of the scene and suppress the regions that are irrelevant to the classification. We tested the proposed EfficientNet-B3-Attn-2 on six popular remote sensing datasets, namely UC Merced, KSA, OPTIMAL-31, RSSCN7, WHU-RS19, and AID datasets, showing its strong capabilities in classifying RS scenes.

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“…We have moved into a Big Data Era [1,2], and an enormous amount of data are expected to be processed to accomplish different special tasks. With rapid remote sensing technology development springing up, optical satellite images are widely used for automatical applications, such as different applications of target detection [3][4][5][6][7][8] and scene classification [9]. However, clouds cover more than 50% of the surface of the earth [10][11][12], and consequently, clouds might be great challenges when automatically processing the images.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Multilevel Features for Cloud Detection on Satellite Imagery

Shi

2018

Remote Sensing

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Cloud detection, which is defined as the pixel-wise binary classification, is significant in satellite imagery processing. In current remote sensing literature, cloud detection methods are linked to the relationships of imagery bands or based on simple image feature analysis. These methods, which only focus on low-level features, are not robust enough on the images with difficult land covers, for clouds share similar image features such as color and texture with the land covers. To solve the problem, in this paper, we propose a novel deep learning method for cloud detection on satellite imagery by utilizing multilevel image features with two major processes. The first process is to obtain the cloud probability map from the designed deep convolutional neural network, which concatenates deep neural network features from low-level to high-level. The second part of the method is to get refined cloud masks through a composite image filter technique, where the specific filter captures multilevel features of cloud structures and the surroundings of the input imagery. In the experiments, the proposed method achieves 85.38% intersection over union of cloud in the testing set which contains 100 Gaofen-1 wide field of view images and obtains satisfactory visual cloud masks, especially for those hard images. The experimental results show that utilizing multilevel features by the combination of the network with feature concatenation and the particular filter tackles the cloud detection problem with improved cloud masks.

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Convolutional Attention in Ensemble With Knowledge Transferred for Remote Sensing Image Classification

Cited by 6 publications

References 19 publications

A lightweight and stochastic depth residual attention network for remote sensing scene classification

A lightweight and stochastic depth residual attention network for remote sensing scene classification

Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention

Utilizing Multilevel Features for Cloud Detection on Satellite Imagery

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