Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier

Boualleg, Yaakoub; Farah, Mohamed; Farah, Imed Riadh

doi:10.1109/lgrs.2019.2911855

Cited by 66 publications

(31 citation statements)

References 12 publications

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“…More recently, algorithms employing deep learning (DL) (i.e., Deep Neural Networks (DNNs)) [12] have also become very popular for LULC classification [13]. Recently, several powerful architectures of DL models have been developed for the classification of RS images [14][15][16]. It is, however, still arguable how well these DL algorithms perform against ensemble algorithms and SVM for the purpose of urban LULC classification.…”

Section: Machine Learning Classifiers For Object-based Classificationmentioning

confidence: 99%

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

2019

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With the advent of high-spatial resolution (HSR) satellite imagery, urban land use/land cover (LULC) mapping has become one of the most popular applications in remote sensing. Due to the importance of context information (e.g., size/shape/texture) for classifying urban LULC features, Geographic Object-Based Image Analysis (GEOBIA) techniques are commonly employed for mapping urban areas. Regardless of adopting a pixel- or object-based framework, the selection of a suitable classifier is of critical importance for urban mapping. The popularity of deep learning (DL) (or deep neural networks (DNNs)) for image classification has recently skyrocketed, but it is still arguable if, or to what extent, DL methods can outperform other state-of-the art ensemble and/or Support Vector Machines (SVM) algorithms in the context of urban LULC classification using GEOBIA. In this study, we carried out an experimental comparison among different architectures of DNNs (i.e., regular deep multilayer perceptron (MLP), regular autoencoder (RAE), sparse, autoencoder (SAE), variational autoencoder (AE), convolutional neural networks (CNN)), common ensemble algorithms (Random Forests (RF), Bagging Trees (BT), Gradient Boosting Trees (GB), and Extreme Gradient Boosting (XGB)), and SVM to investigate their potential for urban mapping using a GEOBIA approach. We tested the classifiers on two RS images (with spatial resolutions of 30 cm and 50 cm). Based on our experiments, we drew three main conclusions: First, we found that the MLP model was the most accurate classifier. Second, unsupervised pretraining with the use of autoencoders led to no improvement in the classification result. In addition, the small difference in the classification accuracies of MLP from those of other models like SVM, GB, and XGB classifiers demonstrated that other state-of-the-art machine learning classifiers are still versatile enough to handle mapping of complex landscapes. Finally, the experiments showed that the integration of CNN and GEOBIA could not lead to more accurate results than the other classifiers applied.

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Section: Machine Learning Classifiers For Object-based Classificationmentioning

confidence: 99%

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

2019

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“…Among the typical deep architectures, CNN provides a strong ability of feature extraction and yield significant performance improvement on scene classification. There have already been several attempts to use deep CNN features for classifying the remote sensing images [3][4][5]21,[42][43][44][45][46][47]. Wang et al [26] employed CaffeNet with the soft-max layer for scene classification.…”

Section: The Deep Learning-based Methodsmentioning

confidence: 99%

“…AlexNet [43] 88.35 ± 0.85 −0.84 CaffeNet [43] 90.48 ± 0.78 +1.29 GoogLeNet [43] 89.19 ± 1.19 0 VGG-16 [42] 90.70 ± 0.68 +1.51 VGG-19 [43] 89.76 ± 0.69 +0.57 ResNet-50 [43] 91.93 ± 0.61 +2.74 ResNet-152 [43] 92. 47 • WHU-RS19 Dataset:…”

Section: Wrt Baselinementioning

confidence: 99%

A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

et al. 2021

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Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

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“…The first is to depend on the pretrained CNNs to extract the feature vectors of HRRS images and then input them into other classifiers for classification [42]- [46]. Boualleg et al used the well-known VGGNet-16 to extract deep features and then fed to deep forest classifier for classification, which saved a considerable amount of training time [43]. Weng et al obtained image feature vectors through AlexNet, then classified images by extreme learning machine.…”

Section: Related Workmentioning

confidence: 99%

Multi-Structure Joint Decision-Making Approach for Land Use Classification of High-Resolution Remote Sensing Images Based on CNNs

Chen

Pan

et al. 2020

IEEE Access

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Land use classification of high-resolution remote sensing (HRRS) images is a challenging and prominent problem in which pretrained convolutional neural networks (CNNs) have made amazing achievements. However, single-structured pretrained CNNs have limitations to obtain high classification accuracy. Besides, each pretrained CNNs has different classification ability to classify land use. Therefore, taking advantages of different pretrained CNNs is essential for land use classification. In this study, we propose a novel classification approach based on multi-structure joint decision-making strategy and pretrained CNNs. The basic idea is to apply three CNNs to classify land use separately with the final classification results achieved by joint decision-making strategy. The proposed approach comprises of three steps. First, we create a new fully connected layer and Softmax classification layer. We combine them with the convolutional layers of AlexNet, Inception-v3, and ResNet18. AlexNet also includes the first two layers of fully connected layers. Secondly, we train these designed CNNs to converge by momentum-driven stochastic gradient descent. Thirdly, we utilize joint decision-making strategy to obtain the final prediction results by combining the prediction results of these designed CNNs. The performance of the proposed approach is evaluated on the UC Merced land use, AID, NWPU-45, OPTIMAL-31 datasets and further compared with the state-of-the-art methods. Results demonstrate that the proposed approach outperforms other methods. The benefits of the proposed approach are threefold. First, the multi-structure network maximizes different pretrained CNN structures to extract rich convolution features. Secondly, it could remarkably improve the classification accuracy of indistinguishable land use types of the HRRS images. Thirdly, it has great potential on small dataset with more land use types. The proposed CNN based on multi-structure joint decision approach achieves accurate and reliable land use classification with these benefits.INDEX TERMS Land use classification, convolutional neural network, transfer learning, high-resolution remote sensing images, multi-structure.

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Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier

Cited by 66 publications

References 12 publications

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification

A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Multi-Structure Joint Decision-Making Approach for Land Use Classification of High-Resolution Remote Sensing Images Based on CNNs

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