Comparison of CNNs for Remote Sensing Scene Classification

Salem, Mohammed A.-M.; Ebeid, Hala M.; Al-Berry, Maryam N.; Tolba, Mohamed F.

doi:10.1109/icces.2018.8639467

Cited by 11 publications

(15 citation statements)

References 21 publications

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“…Extensive experiments on the PatternNet dataset are conducted by the combination of proposed SDAResNet and several tricks. As can be seen in Table 3, our results outperform other comparison approaches [5], [46], [47] with overall accuracy of 99.30% and 99.58% under training ratios of 20% and 50%, respectively. As shown in Table 3, LANet [5] and GLANet(SVM) [5] (98.64% and 98.91% under training ratio of 20%) are superior to all CNN-based single feature methods, which indicates that using attention mechanism is a good strategy to further improve classification accuracy, but still inferior to our SDAResNet.…”

Section: ) Comparative Results On Patternnet Datasetmentioning

confidence: 65%

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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Section: ) Comparative Results On Patternnet Datasetmentioning

confidence: 65%

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

2020

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“…Linear decay learning rate scheduler and cyclical learning rates are used to tune the hyperparameter of the network and label smoothing regularization is used to avoid the overfitting. Shafaey et al [46] explored a deep learning model performance for remote sensing image classification. A comprehensive review is presented by considering the deep learning models such as AlexNet, VGGNet, GoogLeNet, Inception-V3, and ResNet101.…”

Section: Related Workmentioning

confidence: 99%

“…Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and SVM are used for predicting the class labels, and the results are compared with the above-mentioned deep learning models. A detailed quantitative comparison in terms of results is presented by considering seven publicly available datasets [46]. In another research, Zhao et al [47] stated that Residual Dense Network (RDN) is with more learning ability as it can utilize the information available in convolutional layers.…”

Section: Related Workmentioning

confidence: 99%

Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

Shabbir

Ali

Ahmed

et al. 2021

Mathematical Problems in Engineering

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Image classification has gained lot of attention due to its application in different computer vision tasks such as remote sensing, scene analysis, surveillance, object detection, and image retrieval. The primary goal of image classification is to assign the class labels to images according to the image contents. The applications of image classification and image analysis in remote sensing are important as they are used in various applied domains such as military and civil fields. Earlier approaches for remote sensing images and scene analysis are based on low-level feature representations such as color- and texture-based features. Vector of Locally Aggregated Descriptors (VLAD) and orderless Bag-of-Features (BoF) representations are the examples of mid-level approaches for remote sensing image classification. Recent trends for remote sensing and scene classification are focused on the use of Convolutional Neural Network (CNN). Keeping in view the success of CNN models, in this research, we aim to fine-tune ResNet50 by using network surgery and creation of network head along with the fine-tuning of hyperparameters. The learning of hyperparameters is tuned by using a linear decay learning rate scheduler known as piecewise scheduler. To tune the optimizer hyperparameter, Stochastic Gradient Descent with Momentum (SGDM) is used with the usage of weight learn and bias learn rate factor. Experiments and analysis are conducted on five different datasets, that is, UC Merced Land Use Dataset (UCM), RSSCN (the remote sensing scene classification image dataset), SIRI-WHU, Corel-1K, and Corel-1.5K. The analysis and competitive results exemplify that our proposed image classification-based model can classify the images in a more effective and efficient manner as compared to the state-of-the-art research.

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“…1 show images captured by five different satellites; the domain shift occurs according to the specification of the image sensors. Also, Table 1 describes the details of four land-use aerial/satellite datasets, which were collected from different institutions [10], [11]. Given multi-domain images, we aim to build a deep network model to be suited well into the multiple source domains; this problem is referred to as multi-domain learning [12].…”

Section: Introductionmentioning

confidence: 99%

“…In(10), we use the expected squared gradient norm to characterize the convergence rate, as in[54]-[58].VOLUME 4, 2016 …”

mentioning

confidence: 99%

An Alternating Training Method of Attention-Based Adapters for Visual Explanation of Multi-Domain Satellite Images

Kim

Lee

et al. 2021

IEEE Access

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Recently, satellite image analytics based on convolutional neural networks have been vigorously investigated; however, in order for the artificial intelligence systems to be applied in practice, there still exists several challenges: (a) model explanability to improve the reliability of the artificial intelligence system by providing the evidence for the prediction results; (b) dealing with domain shift among images captured by multiple satellites of which the specification of the image sensors is various. To resolve the two issues in the development of a deep model for satellite image analytics, in this paper we propose a multi-domain learning method based on attention-based adapters. As plug-ins to the backbone network, the adapter modules are designed to extract domain-specific features as well as improve visual attention for input images. In addition, we also discuss an alternating training strategy of the backbone network and the adapters in order to effectively separate domain-invariant features and -specific features, respectively. Finally, we utilize Grad-CAM/LIME to provide visual explanation on the proposed network architecture. The experimental results demonstrate that the proposed method can be used to improve test accuracy, and its enhancement in visual explanability is also validated. INDEX TERMSDeep network parametrization, multi-domain learning, satellite image analytics, visual explanation.

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Comparison of CNNs for Remote Sensing Scene Classification

Cited by 11 publications

References 21 publications

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

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

Satellite and Scene Image Classification Based on Transfer Learning and Fine Tuning of ResNet50

An Alternating Training Method of Attention-Based Adapters for Visual Explanation of Multi-Domain Satellite Images

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