POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information

Liu, Fang; Jiao, Licheng; Hou, Biao; Yang, Shuyuan

doi:10.1109/tgrs.2016.2514504

Cited by 158 publications

(84 citation statements)

References 31 publications

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“…Several of the practical applications are based on VHR remote sensing imagery classification at the pixel level [5][6][7][8], also defined as semantic segmentation. Semantic segmentation of remote sensing imagery aims to classify every pixel into a given category, and it is an important task for understanding and inferring objects [9,10] and the relationships between spatial objects in a scene [11].…”

Section: Introductionmentioning

confidence: 99%

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

et al. 2018

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Very high resolution (VHR) remote sensing imagery has been used for land cover classification, and it tends to a transition from land-use classification to pixel-level semantic segmentation. Inspired by the recent success of deep learning and the filter method in computer vision, this work provides a segmentation model, which designs an image segmentation neural network based on the deep residual networks and uses a guided filter to extract buildings in remote sensing imagery. Our method includes the following steps: first, the VHR remote sensing imagery is preprocessed and some hand-crafted features are calculated. Second, a designed deep network architecture is trained with the urban district remote sensing image to extract buildings at the pixel level. Third, a guided filter is employed to optimize the classification map produced by deep learning; at the same time, some salt-and-pepper noise is removed. Experimental results based on the Vaihingen and Potsdam datasets demonstrate that our method, which benefits from neural networks and guided filtering, achieves a higher overall accuracy when compared with other machine learning and deep learning methods. The method proposed shows outstanding performance in terms of the building extraction from diversified objects in the urban district.

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Section: Introductionmentioning

confidence: 99%

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

et al. 2018

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“…Rule (1) indicates that the model is inclined to select easy samples (with smaller training losses). Rule (2) indicates that when the pace parameter λ becomes larger, the model tends to incorporate more complex samples to train.…”

Section: Related Workmentioning

confidence: 99%

“…θ (1) and θ (2) are initialized by the values of W (1,1) and W (2,1) , respectively. Here, b (1,1) and b (2,1) are used to initialize b (1) and b (2) , respectively. Then, apply supervised fine tuning of the multilayer autoencoders network to update the parameters θ (k) and b…”

Section: Supervised Fine-tuning Those Parameters With Softmax Regressionmentioning

confidence: 99%

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Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

et al. 2018

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Abstract:In this paper, a novel polarimetric synthetic aperture radar (PolSAR) image classification method based on multilayer autoencoders and self-paced learning (SPL) is proposed. The multilayer autoencoders network is used to learn the features, which convert raw data into more abstract expressions. Then, softmax regression is applied to produce the predicted probability distributions over all the classes of each pixel. When we optimize the multilayer autoencoders network, self-paced learning is used to accelerate the learning convergence and achieve a stronger generalization capability. Under this learning paradigm, the network learns the easier samples first and gradually involves more difficult samples in the training process. The proposed method achieves the overall classification accuracies of 94.73%, 94.82% and 78.12% on the Flevoland dataset from AIRSAR, Flevoland dataset from RADARSAT-2 and Yellow River delta dataset, respectively. Such results are comparable with other state-of-the-art methods.

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“…DBN [17,18] is organized by a number of restricted Boltzmann machine (RBM) models. The visual layer of the RBM model is similar to the input layer, and the hidden layer is similar to the output layer.…”

Section: Dbn Networkmentioning

confidence: 99%

A Deep Learning Prediction Model Based on Extreme‐Point Symmetric Mode Decomposition and Cluster Analysis

Zhang

Yang

2017

Mathematical Problems in Engineering

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Aiming at the irregularity of nonlinear signal and its predicting difficulty, a deep learning prediction model based on extremepoint symmetric mode decomposition (ESMD) and clustering analysis is proposed. Firstly, the original data is decomposed by ESMD to obtain the finite number of intrinsic mode functions (IMFs) and residuals. Secondly, the fuzzy -means is used to cluster the decomposed components, and then the deep belief network (DBN) is used to predict it. Finally, the reconstructed IMFs and residuals are the final prediction results. Six kinds of prediction models are compared, which are DBN prediction model, EMD-DBN prediction model, EEMD-DBN prediction model, CEEMD-DBN prediction model, ESMD-DBN prediction model, and the proposed model in this paper. The same sunspots time series are predicted with six kinds of prediction models. The experimental results show that the proposed model has better prediction accuracy and smaller error.

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POL-SAR Image Classification Based on Wishart DBN and Local Spatial Information

Cited by 158 publications

References 31 publications

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Classification of PolSAR Images Using Multilayer Autoencoders and a Self-Paced Learning Approach

A Deep Learning Prediction Model Based on Extreme‐Point Symmetric Mode Decomposition and Cluster Analysis

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