Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Li, Weijia; Fu, Haohuan; Yu, Le; Gong, Peng; Feng, Di; Li, Congcong; Clinton, Nicholas

doi:10.1080/01431161.2016.1246775

Cited by 156 publications

(59 citation statements)

References 18 publications

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“…Classification accuracy of the SVM was also improved by including vegetation abundances, although such improvement was not that prominent. Additionally, our results suggest that ANN and RF could achieve similar classification accuracies, which was also confirmed by previous studies [80]. In our case, classification accuracies of ANN and RF were 82.14% and 81.29% before vegetation abundances were added, and increased by 2.86% and 2.92% for ANN and RF, respectively, after vegetation abundances were included.…”

Section: Comparison Of Different Classifier Resultssupporting

confidence: 91%

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Zhou

Chen

et al. 2019

Forests

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Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination.

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Section: Comparison Of Different Classifier Resultssupporting

confidence: 91%

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Zhou

Chen

et al. 2019

Forests

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“…Typical deep learning network structures include the deep belief network which needs to vectorize the raw image and can lead to loss of topology information, the stacked autoencoder (SAE) [33], and convolutional neural networks (CNNs). For a large number of labeled sample datasets, convolutional neural networks (CNNs) is the most effective learning model for feature extraction.…”

Section: Deep-learning-driven Scene Classificationmentioning

confidence: 99%

“…Scene classification mainly includes artificial features (Grey-Level Co-occurrence Matrix (GLCM) [35], Local binary patterns (LBP) [36], Histogram of Oriented Gradient (HOG) [37], Gist [38]), data-driven supervised classification features, and data-driven unsupervised classification features. Typical deep learning network structures include the deep belief network which needs to vectorize the raw image and can lead to loss of topology information, the stacked autoencoder (SAE) [33], and convolutional neural networks (CNNs). For a large number of labeled sample datasets, convolutional neural networks (CNNs) is the most effective learning model for feature extraction.…”

Section: Deep-learning-driven Scene Classificationmentioning

confidence: 99%

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Wang

2019

Applied Sciences

114

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As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.

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“…With the introduction of the concept of deep learning, traditional AEs were extended as SAE by adding multiple layers, usually larger than 2 layers, to form the deep structure of an AE. The detailed content of SAE can be found in [19,20]. x  .…”

Section: Autoencoder-based Network For Clusteringmentioning

confidence: 99%

A Hybrid Autoencoder Network for Unsupervised Image Clustering

Chen

Huang

2019

Algorithms

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Image clustering involves the process of mapping an archive image into a cluster such that the set of clusters has the same information. It is an important field of machine learning and computer vision. While traditional clustering methods, such as k-means or the agglomerative clustering method, have been widely used for the task of clustering, it is difficult for them to handle image data due to having no predefined distance metrics and high dimensionality. Recently, deep unsupervised feature learning methods, such as the autoencoder (AE), have been employed for image clustering with great success. However, each model has its specialty and advantages for image clustering. Hence, we combine three AE-based models—the convolutional autoencoder (CAE), adversarial autoencoder (AAE), and stacked autoencoder (SAE)—to form a hybrid autoencoder (BAE) model for image clustering. The MNIST and CIFAR-10 datasets are used to test the result of the proposed models and compare the results with others. The results of the clustering criteria indicate that the proposed models outperform others in the numerical experiment.

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Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping

Cited by 156 publications

References 18 publications

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

A Hybrid Autoencoder Network for Unsupervised Image Clustering

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