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Hyperspectral image classification using multi-task feature leverage with multi-variant deep learning

EARTH SCIENCE INFORMATICS 2020年第4期13卷 1093-1102页

作者： Jayapriya, Kalyanakumar Jacob, Israel Jeena Darney, Paulraj Ebby Vin Solut Tirunelveli India GITAM Sch Technol Dept Comp Sci & Engn Bengaluru India SCAD Coll Engn & Technol Dept Elect Engn Tirunelveli India

Deep learning framework aids the researchers in learning different application areas to a greater extent. Deep learning framework is preferred over machine learning since it helps to learn the input from end to end, whereas latter one require the inputs to be cut into pieces according to the need. This paper proposes a Multi-Variant Deep Learning framework for learning and classifying the hyperspectral images. Multi-task Feature Leverage is incorporated by doing two-ordered feature extraction. The first order feature extraction was done by using Two Dimensional Empirical Wavelet Transforms (2D-EWT) and the second-order feature extraction was done by using stacked autoencoder (SAE) and Convolutional Neural Network (CNN) for the approximation image of 2D-EWT. Because of the possibility of working on prominent feature, the proposed work uses approximation image than the raw image. The classification was carried out by using Random Forest (RF), Multi- Support Vector Machine (MSVM) and Extreme learning machine (ELM).

关键词： Deep learning Random forest Multi-support vector machine Extreme learning machine stacked autoencoder Convolutional neural network

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Classification of hyperspectral images by deep learning of spectral-spatial features

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ARABIAN JOURNAL OF GEOSCIENCES 2020年第12期13卷 1-14页

作者： Ding, Haiyong Xu, Luming Wu, Yue Shi, Wenzhong Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomat Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing 210044 Peoples R China Hong Kong Polytech Univ Dept Land Surveying & Geoinformat Hong Kong Peoples R China

Creating accurate land use and land cover maps using remote sensing images is one of the most important applications of remotely sensed data. Abundant spectral information in hyperspectral images (HSI) makes it possible to distinguish materials that would not be distinguishable by multi-spectral sensors. Spectral and spatial information from HSI is of primary importance for image classification. In this study, a hybrid stacked autoencoder (SAE) architecture and support vector machine (SVM) classifier was constructed to classify the HSI. The SAE architecture is constituted by stacking a multiple autoencoder (AE) deep learning network that consists in the encoder and decoder process. Spatial features in a neighbor region extracted from the principal component analysis (PCA) and the texture feature extracted from the gray-level cooccurrence matrix (GLCM) were fed into the classifier. It was found that the best result was from the combination of GLCM texture feature, PCA spatial feature, and spectral feature. Meanwhile, the representative features derived from SAE deep learning network were better than the original features. It reminded us that extracting the representative features from hyperspectral images is a key step of improving classification accuracy.

关键词： stacked autoencoder Deep learning Hyperspectral image classification Spatial features

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WiFi-Sensing Based Person-to-Person Distance Estimation Using Deep Learning 24

WiFi-Sensing Based Person-to-Person Distance Estimation Usin...

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24th IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Liu, Wenping Jia, Yufu Jiang, Guoyin Jiang, Hongbo Wu, Fan Lv, Zhicheng Hubei Univ Econ Sch IMS Wuhan Hubei Peoples R China Hunan Univ Coll CSEE Changsha Hunan Peoples R China Univ Elect Sci & Tech China Sch PA Chengdu Sichuan Peoples R China Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China Hubei Univ Econ Sch IE Wuhan Hubei Peoples R China

ISBN: (纸本)9781538673089

Accurately estimating the distance between persons with COTS mobile devices can benefit many applications (e.g., group activity analysis, indoor navigation, etc.). In this paper we present WiDE, a deep learning-based system for estimating person-to-person distance based on surrounding WiFi signals. Specifically, WiDE has two phases: offline learning and on-line prediction. In offline learning phase, we apply a stacked autoencoder (SAE) for pre-training the weights of a deep neural network (DNN), and establish a DNN-based classifier for predicting between-person discretized distance and corridor identity using WiFi signals. During online prediction phase, based on the trained DNN with the SAE and newly uploaded WiFi information, we estimate the corridor identities and the distance between pairwise persons. We validate our system by conducting extensive experiments in a three-floor campus building, and the results show that WiDE achieves the corridor identification accuracy over 98% and the median ranging error of 0.9m and 3.0m for two persons on the same corridor and on different corridors, respectively, which outperforms the state-of-the-art proximity inferring system [1].

关键词： Distance Estimation COTS Device Deep Neural Network stacked autoencoder

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stacked Auto-Encoder for Scalable Indoor Localization in Wireless Sensor Networks 15

Stacked Auto-Encoder for Scalable Indoor Localization in Wir...

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15th IEEE International Wireless Communications and Mobile Computing Conference (IEEE IWCMC)

作者： BelMannoubi, Souad Touati, Haifa Snoussi, Hichem Univ Gabes Fac Sci Gabes Hatem Bettaher IResCoMath Res Unit Gabes Tunisia Univ Technol Troyes CNRS FRE 2848 ICD Troyes France

ISBN: (纸本)9781538677476

In this paper, we propose a Deep Neural Network model based on WiFi-fingerprinting to improve the accuracy of zone location in a multi-building, multi-floor indoor environment. The proposed model is presented as a stacked autoencoder (SAE) to allow efficient reduction of the feature space in order to achieve robust and precise classification. The multi-label classification is used to simplify and reduce the complexity of the learning classification task during the training phase. To achieve a hierarchical classification, we applied an argmax function on the multi-label output to convert the multi-label classification into multi-class classification ones to estimate the building, the floor and the zone identifier. Experimental results show that the proposed model achieves an accuracy of 100% for building, 99.66% for floor and 83.47% for zone location with a test time that does not exceed 10.21s.

关键词： Zoning Localization WiFi-Fingerprint Deep Neural Network stacked autoencoder Multi-Class Multi-Label

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LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED ON FUNCTIONAL CORTICAL ROIS 24

LESION DETECTION USING T1-WEIGHTED MRI: A NEW APPROACH BASED...

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24th IEEE International Conference on Image Processing (ICIP)

作者： Guo, Dazhou Zheng, Kang Wang, Song Univ South Carolina Dept Comp Sci & Engn 301 Main St Columbia SC 29208 USA

ISBN: (纸本)9781509021758

Accurate and precise detection of brain lesions on MR images is important for relating lesion locations to impaired behaviors. In this paper, we propose a method to detect lesion voxels on each functional cortical ROI (Region of Interest) independently using only T1-weighted MR images (T1-MRI). In contrast to existing automatic lesion detection methods, which typically detect lesion voxels on the whole MR image or on gray matter (GM)/white matter (WM), we show that the proposed functional cortical ROI based method can lead to better lesion-detection performance. We evaluate the proposed method using an in-house dataset with 60 chronic stroke patients. Using leave-one-subject-out cross validation, the proposed method can achieve an average Dice coefficient of 0.74 +/- 0.11 and outperform three state-of-the-art methods by more than 0.05.

关键词： Lesion Detection Functional Cortical ROIs T1-weighted MRI stacked autoencoder

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Improve Traffic Prediction Using Accident Embedding on Ensemble Deep Neural Networks 11

Improve Traffic Prediction Using Accident Embedding on Ensem...

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11th Annual International Conference on Knowledge and Smart Technology (KST)

作者： Liyong, Wanida Vateekul, Peerapon Chulalongkorn Univ Fac Engn Dept Comp Engn Big Data Analyt & IoT Ctr CUBIC Bangkok Thailand

ISBN: (纸本)9781538675120

Traffic Prediction in a large-scale network has become an important topic of an intelligent transportation system (ITS). The problem is challenging due to various nonlinear temporal and difficulty for longer-step ahead prediction. The recurring congestion on rush-hour and non-recurring congestion on accidents are the main patterns. While most of the previously proposed techniques focus on rush-hour, unexpected accidents that affect local traffic were not considered in these works. To improve the prediction, we propose an ensemble deep neural network to archive the recurring and non-recurring congestion together combining CNN and LSTM architectures. Furthermore, the accident information is utilized and embedded using stacked autoencoder. An experiment was conducted on two data sets: England's Highways and Thailand's Expressways. The results showed that a combination of CNN and LSTM can be improved by using an embedded vector of accident information.

关键词： Traffic Prediction Convolutional Neural Networks Long Short-Term Memory stacked autoencoder Ensemble

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A Histopathological Image Feature Representation Method based on Deep Learning 7

A Histopathological Image Feature Representation Method base...

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7th International Conference on Information Technology in Medicine and Education (ITME)

作者： Zhang, Gang Zhong, Ling Huang, Yonghui Zhang, Yi Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Affiliated Hosp 1 Dept Plast & Reconstruct Surg Guangzhou 510000 Guangdong Peoples R China

ISBN: (纸本)9781467383028

Automated annotation and grading for histopathological image plays an important role in CAD systems. It provides valuable information and support for medical diagnosis. Currently, computer-aid analysis of histopathological images mainly relies on some well-designed digital features, which requires abundant human efforts and experiences in problem domain. Learning a good feature representation from data can have positive effects on constructing the target model. We propose a novel method for histopathological image feature representation based on deep learning. The method extracts high level representation of raw pixels of a local region through a network model with several hidden layers, which can learn potential features automatically. The proposed method is evaluated on a real data set from a large local hospital with comparison to two current state-of-the-art methods. The result is promising indicating that it achieves significant improvement of the model performance. Moreover, our study suggests that features learned through deep models can achieve better performance than human designed features.

关键词： histopathological image analysis feature representation deep learning stacked autoencoder

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DL-PRO: A Novel Deep Learning Method for Protein Model Quality Assessment

DL-PRO: A Novel Deep Learning Method for Protein Model Quali...

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International Joint Conference on Neural Networks (IJCNN)

作者： Nguyen, Son P. Shang, Yi Xu, Dong Univ Missouri Dept Comp Sci Columbia MO 65211 USA Univ Missouri Christopher S Bond Life Sci Ctr Columbia MO 65211 USA

ISBN: (纸本)9781479914845

Computational protein structure prediction is very important for many applications in bioinformatics. In the process of predicting protein structures, it is essential to accurately assess the quality of generated models. Although many single-model quality assessment (QA) methods have been developed, their accuracy is not high enough for most real applications. In this paper, a new approach based on C-alpha atoms distance matrix and machine learning methods is proposed for single-model QA and the identification of native-like models. Different from existing energy/scoring functions and consensus approaches, this new approach is purely geometry based. Furthermore, a novel algorithm based on deep learning techniques, called DL-Pro, is proposed. For a protein model, DL-Pro uses its distance matrix that contains pairwise distances between two residues' C-alpha atoms in the model, which sometimes is also called contact map, as an orientation-independent representation. From training examples of distance matrices corresponding to good and bad models, DL-Pro learns a stacked autoencoder network as a classifier. In experiments on selected targets from the Critical Assessment of Structure Prediction (CASP) competition, DL-Pro obtained promising results, outperforming state-of-the-art energy/scoring functions, including OPUS-CA, DOPE, DFIRE, and RW.

关键词： deep learning stacked autoencoder protein model quality assessment energy and scoring function classification Critical Assessment of Structure Prediction (CASP)

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A Novel Feature Representation Method Based on Deep Neural Networks for Gear Fault Diagnosis 8

A Novel Feature Representation Method Based on Deep Neural N...

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2017 Prognostics and System Health Management Conference (PHM-Harbin)

作者： Wang, Jinrui Li, Shunming Jiang, Xingxing Xin, Yu Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing Jiangsu Peoples R China Soochow Univ Sch Urban Rail Transpotat Suzhou Peoples R China

ISBN: (纸本)9781538603703

Intelligent diagnosis method has been a hot topic in the prognostics and fault diagnosis of rotating machinery. A deep neural network (DNN) method for gear fault diagnosis based on stacked autoencode (SAE) and softmax regression is presented in this work. SAE is first utilized to extract features from the frequency spectra of vibration signal. And then the learned features are used to train softmax regression classifier for identifying gear faults. The diagnosis results validate that the method is feasible and is able to acquire superior classification performance. With the increasing layers of DNN, the learned features of each hidden layer are becoming more and more robust for classification. In this paper, a novel view on the visualization of the learned features is displayed to evaluate the DNN-based fault diagnosis method.

关键词： fault diagnosis stacked autoencoder softmax regression visualization

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An unsupervised anomaly detection approach based on industrial big data 2

An unsupervised anomaly detection approach based on industri...

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2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)

作者： Zhang, Cong Zhu, Yongsheng Ren, Zhijun Chen, Kaida Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & RotorBearing Xian Peoples R China

ISBN: (纸本)9781728150451

The development of condition monitoring system provides large amounts of operational data. These data present typical characteristics of multiple sources, polymorphism, diversity and mass, and can reflect the service quality and operating state of the equipment. How to use these data becomes one of the main problems in data analysis. To address the issue, this paper presents a generalized model for complex mechanical system anomaly detection based on the data collected from distributed control system (DCS). The stacked Auto-encoder network is used to achieve the automatic extraction of the hidden features in multidimensional polymorphic data. The isolation forest (IF) method is used to achieve the anomaly detection, which only needs to use the DCS monitoring data during normal operation of the unit for network training and model fitting without fault data. And the method can realize the abnormal detection during the operation of the unit without the traditional signal processing method for feature extraction. The proposed method has been used for real compressor. The results show that the proposed approach can detect the anomalies without the need for fault data. And the proposed method is more effective than traditional methods.

关键词： Anomaly detection DCS data stacked autoencoder Isolation Forest

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