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Spectral-spatial feature learning for hyperspectral imagery classification using deep stacked sparse autoencoder

JOURNAL OF APPLIED REMOTE SENSING 2017年第4期11卷

作者： Abdi, Ghasem Samadzadegan, Farhad Reinartz, Peter Univ Tehran Fac Surveying & Geospatial Engn Coll Engn Tehran Iran German Aerosp Ctr DLR Remote Sensing Technol Inst Dept Photogrammetry & Image Anal Wessling Germany

Classification of hyperspectral remote sensing imagery is one of the most popular topics because of its intrinsic potential to gather spectral signatures of materials and provides distinct abilities to object detection and recognition. In the last decade, an enormous number of methods were suggested to classify hyperspectral remote sensing data using spectral features, though some are not using all information and lead to poor classification accuracy;on the other hand, the exploration of deep features is recently considered a lot and has turned into a research hot spot in the geoscience and remote sensing research community to enhance classification accuracy. A deep learning architecture is proposed to classify hyperspectral remote sensing imagery by joint utilization of spectral-spatial information. A stacked sparse autoencoder provides unsupervised feature learning to extract high-level feature representations of joint spectralspatial information;then, a soft classifier is employed to train high-level features and to fine-tune the deep learning architecture. Comparative experiments are performed on two widely used hyperspectral remote sensing data (Salinas and PaviaU) and a coarse resolution hyperspectral data in the long-wave infrared range. The obtained results indicate the superiority of the proposed spectral-spatial deep learning architecture against the conventional classification methods. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： deep features deep learning hyperspectral imagery classification softmax regression spectral-spatial unsupervised feature learning stacked sparse autoencoder

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Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection

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NEURAL COMPUTING & APPLICATIONS 2023年第24期35卷 17883-17898页

作者： Jagat, Rikhi Ram Sisodia, Dilip Singh Singh, Pradeep Natl Inst Technol Raipur Dept Comp Sci & Engn Raipur 492010 India

Web robots are automated computer programs that can be exploited for benign and malicious activities such as website indexing, monitoring, or unauthorized content scraping and scalping. Several methods are available to detect automated web robots through their footprints and behaviors. Although the accuracy and efficiency of existing methods depend highly on the labeled web log data, countless web requests are generated daily with the help of web robots. Exhaustive and accurate manual labeling of reconstructed sessions is time-consuming and challenging. Further, effective detection of web robots is more challenging with unlabeled or partially labeled data. To address the aforementioned issues, we reformulated web robot detection as a semi-supervised learning problem. In this paper, we propose a deep learning-based Semi-Supervised stacked sparse autoencoder (Web-S4AE) for web robot detection. The proposed model uses content-based features and features extracted from web access log data to effectively classify web robots. The experiments were conducted on publicly available web log data from a library and information portal to assess the performance of Web-S4AE. The Web-S4AE model was trained in two phases. The first phase;comprises training the model with unlabeled data to extract the hidden information, and in the second phase, the model is fine-tuned using labeled data. The results suggest that incorporating more unlabeled data can significantly improve the classifier's performance. The Web-S4AE model's performance was also compared with other models such as the Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP).

关键词： Web robot detection Semi-supervised learning Machine learning Deep learning Deep feature extraction stacked sparse autoencoder

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Research on Dung Beetle Optimization Based stacked sparse autoencoder for Network Situation Element Extraction

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IEEE ACCESS 2024年 12卷 24014-24026页

作者： Yang, Yongchao Zhao, Pan Chizhou Univ Sch Big Data & Artificial Intelligence Chizhou 247000 Peoples R China

Network security situation awareness enables networks to actively and effectively defend against network attacks, relying on the extraction of network situation elements as an initial and decisive step. In existing studies, the stacked sparse autoencoder (SSAE) has been employed to extract features from unlabeled network flows. However, obtaining the optimal hyperparameter combination is challenging due to its numerous hyperparameters. To address this issue, we propose a novel approach named DBO-SSAE that leverages dung beetle optimization (DBO) to select the optimal hyperparameters for SSAE automatically. Applied to the well-known UNSW-NB15 dataset, our model yields an optimal feature subset, which is evaluated across various binary classifiers with different metrics. Experimental results demonstrate that our approach improvesaccuracyandF1-measureby 0.2% to 1.5% while reducing the false negative rate(FNR)and false positive rate(FPR) by 0.06% to 7%, surpassing other feature extraction methods on the same classifier for the UNSW-NB15 dataset. Particularly, in conjunction with a lightweight bidirectional longs hort-term memory (BiLSTM), our model achieves metrics of 98.84%accuracy, 98.96%F1-measure, 1.86%FNR, and 0.6%FPR. This study could provide novel insights into the effective representation of network situation elements and lay the groundwork for a high-efficiency intrusion detection system

关键词： Feature extraction Principal component analysis Optimization Intrusion detection Telecommunication traffic Classification algorithms Support vector machines Encoding Network security Dung beetle optimization network security network situation element extraction stacked sparse autoencoder

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Spectral-spatial classification of hyperspectral images using trilateral filter and stacked sparse autoencoder

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Zhao, Chunhui Wan, Xiaoqing Zhao, Genping Yan, Yiming Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

A spectral-spatial classification method using a trilateral filter (TF) and stacked sparse autoencoder (SSA) for improving the classification accuracy of hyperspectral image (HSI) is proposed. The operation is carried out in two main stages: edge-preserved smoothing and high-level feature learning. First, a reference image obtained from dual tree complex wavelet transform is adopted in a TF for smoothing the HSI. As expected, the filter not only can effectively attenuate the mixed noise (e. g., Gaussian noise and impulse noise) where the bilateral filter shows poor performance but also can produce useful spectral-spatial features from HSI by considering geometric closeness and photometric similarity between pixels simultaneously. Second, an artificial fish swarm algorithm (AFSA) is first introduced into a SSA, and the proposed deep learning architecture is used to adaptively exploit more abstract and differentiable high-level feature representations from the smoothed HSI, based on the factor that AFSA provides better trade-off among concurrency, search efficiency, and convergence rate compared with gradient descent and back-propagation algorithms in a traditional SSA. Finally, a random forest classifier is utilized to perform supervised fine-tuning and classification. Experimental results on two real HSI data sets demonstrate that the proposed method generates competitive performance compared with those of conventional methods. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： classification hyperspectral imagery spatial filtering artificial fish swarm algorithm stacked sparse autoencoder

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Five-category classification of pathological brain images based on deep stacked sparse autoencoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第4期78卷 4045-4064页

作者： Jia, Wenjuan Muhammad, Khan Wang, Shui-Hua Zhang, Yu-Dong Nanjing Normal Univ Sch Comp Sci & Engn 1 Wenyuan Nanjing 210023 Jiangsu Peoples R China Sejong Univ Coll Software Convergence Dept Software Intelligent Media Lab Seoul South Korea Univ Leicester Dept Informat Leicester LE1 7RH Leics England

Magnetic resonance imaging (MRI) is employed in medical treatment broadly, due to the quick development of computer technology. It is beneficial to classify the pathological brain images into healthy or other different categories automatically and accurately. This work aims to generate a pathological brain detecting system to classify the pathological brain images into five different categories of healthy;cerebrovascular disease;neoplastic disease;degenerative disease;and inflammatory disease. Our proposed method can be composed of the following several steps: First, we used data augmentation technology to deal with unbalanced distribution of the dataset. Then, we used deep stacked sparse autoencoder with minibatch scaled conjugate gradient to train the network, and the softmax layer is used as the classifier. As a result, the accuracy of our deep stacked sparse autoencoder over the test set is 98.6%. The prediction time of each image in test stage is only 0.070s. Our experiment will be a powerful proof of the effectiveness of our proposed method that based on deep stacked sparse autoencoder.

关键词： Data augmentation stacked sparse autoencoder Minibatch scaled gradient descent

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Pseudo Zernike Moment and Deep stacked sparse autoencoder for COVID-19 Diagnosis

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Computers, Materials & Continua 2021年第12期69卷 3145-3162页

作者： Yu-Dong Zhang Muhammad Attique Khan Ziquan Zhu Shui-Hua Wang School of Informatics University of LeicesterLeicesterLE17RHUK Department of Computer Science HITEC University TaxilaTaxilaPakistan Science in Civil Engineering University of FloridaGainesvilleFloridaFL 32608GainesvilleUSA School of Mathematics and Actuarial Science University of LeicesterLE17RHUK

(Aim)COVID-19 is an ongoing infectious *** has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/*** computer vision methods have achieved promising results on the automatic smart diagnosis.(Method)This study aims to propose a novel deep learning method that can obtain better *** use the pseudo-Zernike moment(PZM),derived from Zernike moment,as the extracted *** settings are introducing:(i)image plane over unit circle;and(ii)image plane inside the unit ***,we use a deep-stacked sparse autoencoder(DSSAE)as the ***,multiple-way data augmentation is chosen to overcome *** multiple-way data augmentation is based on Gaussian noise,salt-and-pepper noise,speckle noise,horizontal and vertical shear,rotation,Gamma correction,random translation and scaling.(Results)10 runs of 10-fold cross validation shows that our PZM-DSSAE method achieves a sensitivity of 92.06%±1.54%,a specificity of 92.56%±1.06%,a precision of 92.53%±1.03%,and an accuracy of 92.31%±1.08%.Its F1 score,MCC,and FMI arrive at 92.29%±1.10%,84.64%±2.15%,and 92.29%±1.10%,*** AUC of our model is 0.9576.(Conclusion)We demonstrate“image plane over unit circle”can get better results than“image plane inside a unit circle.”Besides,this proposed PZM-DSSAE model is better than eight state-of-the-art approaches.

关键词： Pseudo Zernike moment stacked sparse autoencoder deep learning COVID-19 multiple-way data augmentation medical image analysis

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PolSAR image classification based on multi-scale stacked sparse autoencoder

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NEUROCOMPUTING 2019年 351卷 167-179页

作者： Zhang, Lu Jiao, Licheng Ma, Wenping Duan, Yiping Zhang, Dan Xidian Univ Int Res Ctr Intelligent Percept & Computat Joint Int Res Lab Intelligent Percept & Computat Minist EducKey Lab Intelligent Percept & Image U Xian 710071 Shaanxi Peoples R China Tsinghua Univ Dept Elect Engn Beijing Peoples R China

Recently, many deep learning methods are applied with the spatial information to learn features for polarimetric synthetic aperture radar (PolSAR) image classification. However, without considering the multi-scale information, the classification performance of these methods are limited. Hence, this paper proposes a multi-scale feature extraction method based on stacked sparse autoencoder (SSAE), named the multi-scale SSAE (MS-SSAE), to improve the classification performance. This method extracts the deep multi-scale features by a two-stage framework. In the first stage, the SSAE uses training data at different scales to extract the multi-scale features. Then, a 1-D average pooling strategy is proposed to reduce the feature dimensionality at the second stage. Therefore, the MS-SSAE can capture discriminative multi-scale features. The experimental results certify that the MS-SSAE can not only improve the classification accuracy, but also remain the details in the image. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Polsar classification Multi-scale stacked sparse autoencoder Deep learning Feature learning

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Image fusion based on shift invariant shearlet transform and stacked sparse autoencoder

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JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 2018年第2期12卷 73-84页

作者： Wang, Peng-Fei Luo, Xiao-Qing Li, Xin-Yi Zhang, Zhan-Cheng Jiangnan Univ Sch IoT Engn Wuxi Peoples R China Suzhou Univ Sci & Technol Coll EIE Suzhou Peoples R China

stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform;second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands;third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients;then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients;finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.

关键词： Image fusion stacked sparse autoencoder shift invariant shearlet transform feature extraction

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Intrusion Detection Method Based on stacked sparse autoencoder and Sliced GRU for Connected Healthcare Systems

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第2期48卷 2061-2074页

作者： Gu, Zhaoyang Wang, Liangliang Li, Jinguo Wen, Mi Liu, Yuping Shanghai Univ Elect Power Coll Comp Technol & Sci Shanghai 201306 Peoples R China Nanjing Inst Technol Coll Comp Technol & Sci Nanjing 211100 Peoples R China

Connected healthcare systems face more and more cyber attacks recently. With the development of technology, people use intrusion detection systems (IDS) to detect network attacks and achieve effective results. The existing methods do not take into account the limited storage and computing power of wireless devices on connected healthcare systems. IDSs in the connected healthcare systems need to be real-time and lightweight. This paper proposes an IDS method based on stacked sparse autoencoder (sSAE) and sliced gated recurrent unit (SGRU). The sSAE reduces the dimensionality of the original traffic data and the memory required to calculate the covariance matrix. We slice the original data and input the processed data into the SGRU networks which are paralleled. Therefore, SGRU networks achieve real-time response. The method uses the AWID dataset. The experimental results show that our scheme is better than deep neural network (DNN), recurrent neural network (RNN), long short-term memory (LSTM) and other methods. Especially, the F1-score of the method is 2-5% higher than existing schemes, the detection time is 5-13 times shorter than other solutions, and the model size is smaller than the size of other models by at least 4 times.

关键词： Connected healthcare systems stacked sparse autoencoder Sliced GRU Intrusion detection

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Classification of PolSAR Images Based on Adaptive Nonlocal stacked sparse autoencoder

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2018年第7期15卷 1050-1054页

作者： Hu, Yuanyuan Fan, Jianchao Wang, Jun Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Natl Marine Environm Monitoring Ctr Dalian 116023 Peoples R China

Land cover classification using polarimetric synthetic aperture radar (PolSAR) images is an important tool for remote sensing analysis. In view that PolSAR image effective interpretation is commonly affected by the absence of discriminative features and the presence of speckle noises, this letter proposes an adaptive nonlocal stacked sparse autoencoder (ANSSAE) to achieve PolSAR image classification. It extracts the adaptive nonlocal spatial information by adaptively calculating weighted average values of each pixel from nonlocal regions, which can reduce the influence of speckle noises and retain edge details. In the first layer of the ANSSAE, the adaptive nonlocal spatial information is introduced into the objective function to obtain the robust feature representation, whose effects would transfer to the rest of layers. Therefore, the ANSSAE can automatically capture spatial-related, robust, and distinguishable features, which can suppress speckle noises and gain accurate classification results. Experimental results on two real PolSAR images demonstrate that the proposed approach can significantly improve the classification accuracy.

关键词： Adaptive nonlocal spatial information polarimetric synthetic aperture radar (PolSAR) image classification stacked sparse autoencoder

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