The Experts below are selected from a list of 479346 Experts worldwide ranked by ideXlab platform
Ma Wenping - One of the best experts on this subject based on the ideXlab platform.
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polarization sar terrain Classification Method based on deep learning and distance metric learning
2016Co-Authors: Jiao Licheng, Ma Wenping, Wang Mingjie, Ma Jingjing, Hou Biao, Yang Shuyuan, Liu Hongying, Feng Jie, Wang RongfangAbstract:The invention discloses a polarization SAR terrain Classification Method based on deep learning and distance metric learning. The polarization SAR terrain Classification Method comprises the realization steps that (1) images are inputted; (2) filtering is performed; (3) features are extracted; (4) training samples and test samples are selected; (5) a stacked sparse auto-encoder is trained so that the deep features of a training sample set and the deep features of a test sample set are obtained; (6) a distance metric learning classifier is trained so that a Classification result is obtained; (7) the Classification result is colored; and (8) the colored Classification result graph is outputted. The images are classified by using the polarization SAR terrain Classification Method based on deep learning and distance metric learning so that feature extraction is relatively comprehensive and reasonable, the Classification result is more consistent with real terrains, time complexity is reduced and Classification precision is enhanced.
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deep level feature learning and watershed based synthetic aperture radar sar image Classification Method
2015Co-Authors: Hou Biao, Jiao Licheng, Ma Wenping, Ma Jingjing, Liu He, Yao Ruoyu, Zhang Tao, Liu ChuangAbstract:The invention discloses a deep-level feature learning and watershed-based synthetic aperture radar (SAR) image Classification Method and belongs to the image processing technical field. The main objective of the invention is to solve the problems of high possibility of wrong Classification of fields, poor region consistency and boundary burrs when middle-and-lower-layer features are applied to SAR image Classification. The Classification Method includes the following steps that: watershed over-segmentation class label L calculation is performed on an inputted SAR image; Gabor features F1 of the inputted SAR image are calculated; after sampling is performed on the F1, sampled F1 are inputted into a K-mean singular value decomposition (KSVD) algorithm, so that a training dictionary D can be obtained; convolution and maximum value pooling are performed on the F1 and the D, so that convolution features F2 can be obtained; the F2 are inputted into a sparse auto-encoder, so that deep-level features F3 can be obtained; the F3 are inputted into a SVM so as to be classified, and Classification results R1 can be obtained; and vote statistics is performed on the R1 at each sub block of watershed segmentation results, so that final Classification results can be obtained. The deep-level feature learning and watershed-based SAR image Classification Method of the invention has the advantages of high computation speed, accurate edge Classification and high region consistency, and can be applied to SAR target recognition.
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polarization sar image Classification Method based on deep neural network
2014Co-Authors: Hou Biao, Jiao Licheng, Kou Hongda, Wang Shuang, Zhang Xiangrong, Ma WenpingAbstract:The invention discloses a polarization SAR image Classification Method based on a deep neural network. The Method mainly solves the problems that traditional polarization SAR image Classification accuracy is low and boundaries are disorderly. The Method includes the Classification steps that a power diagram I is acquired from polarization SAR data through Pauli decomposition, the power diagram I is segmented in advance, and then a plurality of small blocks are acquired; a training data set U is selected from a polarization SAR image, input into a two-layer self-coding structure for training and then classified through a Softmax classifier; a test data set V is selected from the polarization SAR image and input into the trained two-layer self-coding structure, and then Classification labels are acquired through the Softmax classifier; in the pre-segmented small blocks, the Classification labels and channel information of the power diagram I are combined, and then small block labels are acquired. The polarization SAR image Classification Method has the advantages that the recognition rate is high, result region consistency is good, and the Method can be used for polarization SAR homogeneous region terrain Classification.
Hou Biao - One of the best experts on this subject based on the ideXlab platform.
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polarization sar terrain Classification Method based on deep learning and distance metric learning
2016Co-Authors: Jiao Licheng, Ma Wenping, Wang Mingjie, Ma Jingjing, Hou Biao, Yang Shuyuan, Liu Hongying, Feng Jie, Wang RongfangAbstract:The invention discloses a polarization SAR terrain Classification Method based on deep learning and distance metric learning. The polarization SAR terrain Classification Method comprises the realization steps that (1) images are inputted; (2) filtering is performed; (3) features are extracted; (4) training samples and test samples are selected; (5) a stacked sparse auto-encoder is trained so that the deep features of a training sample set and the deep features of a test sample set are obtained; (6) a distance metric learning classifier is trained so that a Classification result is obtained; (7) the Classification result is colored; and (8) the colored Classification result graph is outputted. The images are classified by using the polarization SAR terrain Classification Method based on deep learning and distance metric learning so that feature extraction is relatively comprehensive and reasonable, the Classification result is more consistent with real terrains, time complexity is reduced and Classification precision is enhanced.
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deep level feature learning and watershed based synthetic aperture radar sar image Classification Method
2015Co-Authors: Hou Biao, Jiao Licheng, Ma Wenping, Ma Jingjing, Liu He, Yao Ruoyu, Zhang Tao, Liu ChuangAbstract:The invention discloses a deep-level feature learning and watershed-based synthetic aperture radar (SAR) image Classification Method and belongs to the image processing technical field. The main objective of the invention is to solve the problems of high possibility of wrong Classification of fields, poor region consistency and boundary burrs when middle-and-lower-layer features are applied to SAR image Classification. The Classification Method includes the following steps that: watershed over-segmentation class label L calculation is performed on an inputted SAR image; Gabor features F1 of the inputted SAR image are calculated; after sampling is performed on the F1, sampled F1 are inputted into a K-mean singular value decomposition (KSVD) algorithm, so that a training dictionary D can be obtained; convolution and maximum value pooling are performed on the F1 and the D, so that convolution features F2 can be obtained; the F2 are inputted into a sparse auto-encoder, so that deep-level features F3 can be obtained; the F3 are inputted into a SVM so as to be classified, and Classification results R1 can be obtained; and vote statistics is performed on the R1 at each sub block of watershed segmentation results, so that final Classification results can be obtained. The deep-level feature learning and watershed-based SAR image Classification Method of the invention has the advantages of high computation speed, accurate edge Classification and high region consistency, and can be applied to SAR target recognition.
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polarization sar image Classification Method based on deep neural network
2014Co-Authors: Hou Biao, Jiao Licheng, Kou Hongda, Wang Shuang, Zhang Xiangrong, Ma WenpingAbstract:The invention discloses a polarization SAR image Classification Method based on a deep neural network. The Method mainly solves the problems that traditional polarization SAR image Classification accuracy is low and boundaries are disorderly. The Method includes the Classification steps that a power diagram I is acquired from polarization SAR data through Pauli decomposition, the power diagram I is segmented in advance, and then a plurality of small blocks are acquired; a training data set U is selected from a polarization SAR image, input into a two-layer self-coding structure for training and then classified through a Softmax classifier; a test data set V is selected from the polarization SAR image and input into the trained two-layer self-coding structure, and then Classification labels are acquired through the Softmax classifier; in the pre-segmented small blocks, the Classification labels and channel information of the power diagram I are combined, and then small block labels are acquired. The polarization SAR image Classification Method has the advantages that the recognition rate is high, result region consistency is good, and the Method can be used for polarization SAR homogeneous region terrain Classification.
Hanxiang Zhao - One of the best experts on this subject based on the ideXlab platform.
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a dynamic rock mass Classification Method for tbm tunnel
GeoShanghai International Conference, 2018Co-Authors: Xing Li, Zhenxing Diao, Feng Zhao, Hanxiang ZhaoAbstract:As the traditional rock mass Classification is not suitable for TBM tunnel, the comprehensive rock mass Classification for TBM is established by combing the boreability Classification and adaptability Classification, which are accomplished respectively by analyzing TBM performance. In order to evaluate surrounding rock continuously, Markov Chain Method is used in this paper. Four geological parameters are chosen to describe the ground conditions. Based on YHJW project, we can get the state of each parameter through geological investigation. Counting the state transformation along the alignment at 50 m intervals, the state transformation probability matrix of each parameter is obtained. Comparing the parameter state in the same position between investigation data and revealed condition, the likelihood matrix of each parameter in Qinling Region is acquired. Probability of boreability Classification of K29 + 900~K31 + 150 is computed and the expectation of thrust is considered as the thrust in prediction. The high penetration rate appears when the field thrust is in the range of predicted thrust. Dynamic Rock Mass Classification Method is proved effective. Tunneling parameters can be predicted preliminary through the new Classification.
Jiao Licheng - One of the best experts on this subject based on the ideXlab platform.
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polarization sar terrain Classification Method based on deep learning and distance metric learning
2016Co-Authors: Jiao Licheng, Ma Wenping, Wang Mingjie, Ma Jingjing, Hou Biao, Yang Shuyuan, Liu Hongying, Feng Jie, Wang RongfangAbstract:The invention discloses a polarization SAR terrain Classification Method based on deep learning and distance metric learning. The polarization SAR terrain Classification Method comprises the realization steps that (1) images are inputted; (2) filtering is performed; (3) features are extracted; (4) training samples and test samples are selected; (5) a stacked sparse auto-encoder is trained so that the deep features of a training sample set and the deep features of a test sample set are obtained; (6) a distance metric learning classifier is trained so that a Classification result is obtained; (7) the Classification result is colored; and (8) the colored Classification result graph is outputted. The images are classified by using the polarization SAR terrain Classification Method based on deep learning and distance metric learning so that feature extraction is relatively comprehensive and reasonable, the Classification result is more consistent with real terrains, time complexity is reduced and Classification precision is enhanced.
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deep level feature learning and watershed based synthetic aperture radar sar image Classification Method
2015Co-Authors: Hou Biao, Jiao Licheng, Ma Wenping, Ma Jingjing, Liu He, Yao Ruoyu, Zhang Tao, Liu ChuangAbstract:The invention discloses a deep-level feature learning and watershed-based synthetic aperture radar (SAR) image Classification Method and belongs to the image processing technical field. The main objective of the invention is to solve the problems of high possibility of wrong Classification of fields, poor region consistency and boundary burrs when middle-and-lower-layer features are applied to SAR image Classification. The Classification Method includes the following steps that: watershed over-segmentation class label L calculation is performed on an inputted SAR image; Gabor features F1 of the inputted SAR image are calculated; after sampling is performed on the F1, sampled F1 are inputted into a K-mean singular value decomposition (KSVD) algorithm, so that a training dictionary D can be obtained; convolution and maximum value pooling are performed on the F1 and the D, so that convolution features F2 can be obtained; the F2 are inputted into a sparse auto-encoder, so that deep-level features F3 can be obtained; the F3 are inputted into a SVM so as to be classified, and Classification results R1 can be obtained; and vote statistics is performed on the R1 at each sub block of watershed segmentation results, so that final Classification results can be obtained. The deep-level feature learning and watershed-based SAR image Classification Method of the invention has the advantages of high computation speed, accurate edge Classification and high region consistency, and can be applied to SAR target recognition.
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polarization sar image Classification Method based on deep neural network
2014Co-Authors: Hou Biao, Jiao Licheng, Kou Hongda, Wang Shuang, Zhang Xiangrong, Ma WenpingAbstract:The invention discloses a polarization SAR image Classification Method based on a deep neural network. The Method mainly solves the problems that traditional polarization SAR image Classification accuracy is low and boundaries are disorderly. The Method includes the Classification steps that a power diagram I is acquired from polarization SAR data through Pauli decomposition, the power diagram I is segmented in advance, and then a plurality of small blocks are acquired; a training data set U is selected from a polarization SAR image, input into a two-layer self-coding structure for training and then classified through a Softmax classifier; a test data set V is selected from the polarization SAR image and input into the trained two-layer self-coding structure, and then Classification labels are acquired through the Softmax classifier; in the pre-segmented small blocks, the Classification labels and channel information of the power diagram I are combined, and then small block labels are acquired. The polarization SAR image Classification Method has the advantages that the recognition rate is high, result region consistency is good, and the Method can be used for polarization SAR homogeneous region terrain Classification.
Maaref Hichem - One of the best experts on this subject based on the ideXlab platform.
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Multi-model Classification Method in heterogeneous image databases
Pattern Recognition, 2015Co-Authors: Kachouri Rostom, Djemal Khalifa, Maaref HichemAbstract:Automatic heterogeneous image recognition is a challenging research topic in computer vision. In fact, a general purpose images require multiple descriptors to cover their diverse category contents. However, not all extracted features are always relevant. Furthermore, simply concatenating multiple features may not be efficient for recognizing images in heterogeneous databases. In this context, we propose a new heterogeneous image recognition system, which aims to enhance the recognition results while decreasing the computational complexity. In particular, the proposed system is based on two complementary Methods: adaptive relevant feature selection and multi-model Classification Method (MM-CM). Since it employs hierarchically selected features, the MM-CM ensures better Classification accuracy and significantly less computation time than existing Classification Methods. The performance of the proposed image recognition system is assessed through two image databases and a large number of features. A comparison with competing algorithms from the literature is also provided. Our extensive experimental results show that an adaptive feature selection based MM-CM outperforms existing Methods and improves the Classification results in heterogeneous image databases.
