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DAAN: A Deep autoencoder-Based Augmented Network for Blind Multilinear Hyperspectral Unmixing

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Su, Yuanchao Zhu, Zhiqing Gao, Lianru Plaza, Antonio Li, Pengfei Sun, Xu Xu, Xiang Xian Univ Sci & Technol Coll Geomat Dept Remote Sensing Xian 710054 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China Xian Univ Sci & Technol Coll Geomat Xian 710054 Peoples R China Univ Extremadura Escuela Politecnica Dept Technol Comp & Commun Hyperspectral Comp Lab Caceres 10071 Spain Univ Elect Sci & Technol China Zhongshan Inst Artificial Intelligence & Comp Vis Lab Zhongshan 528402 Peoples R China

In recent years, deep learning (DL) has accelerated the development of hyperspectral image (HSI) processing, expanding the range of applications further. As a typical model of unsupervised DL, the autoencoder framework has been extensively applied for spectral unmixing due to its strong representation ability and scalability. Nowadays, most DL-based unmixing approaches adopt the linear mixture model (LMM) to estimate pure spectral signatures (endmembers) and their corresponding abundance fractions. However, since sunlight scattering is an inevitable physical phenomenon, the spectral mixture problem is inherently nonlinear. Moreover, most existing nonlinear unmixing approaches focus exclusively on spectral information, neglecting the spatial distribution of materials and the intrinsic correlation between pixels, making it challenging to explore latent features. To address these issues, this article develops a new deep autoencoder-based augmented network (DAAN). The proposed DAAN employs the multilinear mixture model (MLMM) to handle the nonlinear influence caused by multiple scattering. Meanwhile, the proposed DAAN constraints homogenous smoothing in the autoencoder architecture, enabling the aggregation of intrinsic correlations by means of spatial relationships to enhance the performance of abundance estimation. We achieve unsupervised nonlinear hyperspectral unmixing by combining spectral and spatial information. The effectiveness and advantages of DAAN are confirmed by several experiments with synthetic and real HSI datasets. The results indicate that the proposed method outperforms other DL-based unmixing approaches. The source codes of the proposed DAAN will be provided in the following link https://***/yuanchaosu/TGRS-daan.

关键词： Scattering Hyperspectral imaging Mixture models Convolutional neural networks Photonics Correlation Spatial resolution autoencoder deep learning (DL) hyperspectral remote sensing nonlinear unmixing

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Classification model of electricity consumption behavior based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 158卷

作者： Huang, Yifan Yao, Zhengnan Xu, Qifeng Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350116 Fujian Peoples R China

The development of electrical measurement technology has brought high latitude residential electricity consumption data to power companies, which contains the characteristics of users' electricity consumption behavior and provides data support for the behavior classification. In order to improve the efficiency of data feature extraction and the accuracy of electricity consumption behavior identification, a classification model based on sparse denoising autoencoder feature dimensionality reduction and spectral clustering is proposed in this paper. Firstly, the sparse denoising autoencoder (SDAE) and the manually defining electricity consumption characteristic indicators are deployed to extract features from the residential daily electricity consumption data, and then the spectral clustering is employed to classify the extracted electricity consumption characteristics. Secondly, the t-distributed stochastic neighbor embedding (t-SNE) is applied to visualize and analyze the classification results, and on this basis, the secondary classification is implemented to fix the issue of the confused electricity consumption behaviors. Finally, the typical consumption behavior curves are calculated by Gaussian distance weighting method, and the characteristics of power consumption behavior are analyzed and summarized. The proposed approach is evaluated and verified by using the electricity dataset in Fujian, China.

关键词： Electricity consumption behavior classification Feature extraction autoencoder Clustering Secondary classification

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Anomaly detection using large-scale multimode industrial data: An integration method of nonstationary kernel and autoencoder

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 131卷

作者： Wang, Kai Yan, Caoyin Mo, Yanfang Wang, Yalin Yuan, Xiaofeng Liu, Chenliang Cent South Univ Sch Automat Changsha 410083 Peoples R China City Univ Hong Kong Hong Kong Inst Data Sci HKIDS Tat Chee Ave Hong Kong 999077 Peoples R China

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling methods and have been widely applied to industrial process monitoring. However, they both present imperfect properties, so the relevant applications are limited. On the one hand, kernels are not so reconstructable, scalable, and robust to hyperparameters that they suffer performance degradation for large-scale data modeling and monitoring. On the other hand, the high-dimensional parameter space of NNs that is sorted to parameter initialization presents severe anomaly detection performance inconsistency, which makes the industry cautious about using NNs. Motivated by these facts, we propose to integrate kernels and NNs, forming a new model structure that is scalable, reconstructable, and performance-consistent. Specifically, a novel autoencoder-based nonstationary pattern selection kernel (AE-NPSK) is proposed by (1) selecting from the training set the critical edges and interior data as the centers of the radial basis functions in the hidden layers and (2) adaptively adjusting the kernel width in the training procedure. Also, the new NN has strong performance consistency, which facilitates the search for optimal parameters. Finally, we test the performance of the proposed method on the challenging multimode processes. The results validate the efficacy of the proposed method.

关键词： Kernel method Artificial neural network Process monitoring Multimode process autoencoder Radial basis function

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Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional autoencoder: A Unified Solution

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SENSORS 2024年第10期24卷 3161页

作者： Han, Ming Dang, Yu Han, Jianda Nankai Univ Inst Robot & Automatic Informat Syst Coll Artificial Intelligence Tianjin 300350 Peoples R China Nankai Univ Engn Res Ctr Trusted Behav Intelligence Minist Educ Tianjin 300350 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300350 Peoples R China Nankai Univ Inst Intelligence Technol & Robot Syst Shenzhen Res Inst Shenzhen 518083 Peoples R China

Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation.

关键词： autoencoder convolutional neural network preprocessing

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Refrigerated Showcase Fault Detection by an autoencoder with the Adaptive Kernel Size Tuning using Maximum Correntropy Criterion

Refrigerated Showcase Fault Detection by an Autoencoder with...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Igarashi, Msato Fukuyama, Yoshikazu Shimasaki, Yuichi Osada, Yuto Murakami, Kenya Iizaka, Tatsuya Santana, Adamo Matsui, Tetsuro Meiji Univ Grad Sch Adv Math Sci Tokyo Japan Fuji Elect Co Ltd Corp R&D Headquarters Tokyo Japan

ISBN: (纸本)9781665487689

This paper proposes refrigerated showcase fault detection by an autoencoder with the adaptive kernel size tuning (AKST) using Maximum Correntropy Criterion (MCC) and Stochastic Gradient Descent with Momentum. Refrigerated showcase data may include outliers that store values differing from actual values because of various reasons such as incorrect sensor settings and radio frequency interference. The conventional fault detection methods by an autoencoder using Least Square Error (LSE) as a loss function are affected by the outliers when the outliers are included in learning data. On the contrary, the conventional fault detection methods by an Artificial Neural Network (ANN) using MCC as a loss function can ignore influence of the outliers. Moreover, faults rarely occur in refrigerated showcases. The methods by an ANN using MCC require fault data for learning. A fault detection method by an autoencoder using the MCC that is a combination of the above two methods can ignore influence of the outliers and doesn't require fault data. However, there is a parameter named kernel size in the MCC. It is required to tune the parameter properly and the parameter tuning requires engineering. The proposed AKST method reduces the engineering of the kernel size tuning. Effectiveness of the proposed method is confirmed by comparison with comparative methods by an autoencoder using LSE and Stochastic Gradient Descent (SGD), an autoencoder with fixed kernel size using the MCC and SGD, an autoencoder with the AKST using the MCC and Adam, and an autoencoder with the AKST using the MCC and AdaGrad.

关键词： refrigerated showcase fault detection outlier handling Maximum Correntropy Criterion adaptive kernel size tuning autoencoder

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MAPLE: Masked Pseudo-Labeling autoencoder for Semi-supervised Point Cloud Action Recognition 22

MAPLE: Masked Pseudo-Labeling autoEncoder for Semi-supervise...

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30th ACM International Conference on Multimedia (MM)

作者： Chen, Xiaodong Liu, Wu Liu, Xinchen Zhang, Yongdong Han, Jungong Mei, Tao Univ Sci & Technol China Hefei Peoples R China JD Explore Acad Beijing Peoples R China Aberystwyth Univ Aberystwyth Dyfed Wales

ISBN: (纸本)9781450392037

Recognizing human actions from point cloud videos has attracted tremendous attention from both academia and industry due to its wide applications like automatic driving, robotics, and so on. However, current methods for point cloud action recognition usually require a huge amount of data with manual annotations and a complex backbone network with high computation cost, which makes it impractical for real-world applications. Therefore, this paper considers the task of semi-supervised point cloud action recognition. We propose a Masked Pseudo-Labeling autoencoder (MAPLE) framework to learn effective representations with much fewer annotations for point cloud action recognition. In particular, we design a novel and efficient Decoupled spatial-temporal TransFormer (DestFormer) as the backbone of MAPLE. In DestFormer, the spatial and temporal dimensions of the 4D point cloud videos are decoupled to achieve an efficient self-attention for learning both long-term and short-term features. Moreover, to learn discriminative features from fewer annotations, we design a masked pseudo-labeling autoencoder structure to guide the DestFormer to reconstruct features of masked frames from the available frames. More importantly, for unlabeled data, we exploit the pseudo-labels from the classification head as the supervision signal for the reconstruction of features from the masked frames. Finally, comprehensive experiments demonstrate that MAPLE achieves superior results on three public benchmarks and outperforms the state-of-the-art method by 8.08% accuracy on the MSR-Action3D dataset. (1)

关键词： Point Cloud Action Recognition Semi-supervised Learning autoencoder Vision Transformer

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Signal Interference Elimination Based on autoencoder for Integrated Sensing and Communication

Signal Interference Elimination Based on Autoencoder for Int...

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International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Jing, Zexuan Mu, Junsheng Jing, Xiaojun Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781665469012

Recently, the communication system has evolved from 5G to 6G, and the integration of communication sensing is a major trend. In order to jointly optimize the communication and sensing performance and eliminate the interference in the transmission process, this paper proposes a signal interference cancellation scheme based on autoencoder to improve the signal quality of the communication signal and the sensing signal. The proposed algorithm model can effectively unify the transmission data formats of different structural forms, and perform joint reconstruction in a general way. In particular, an Inception model is added to the decoder section to improve performance. Based on the simulation software, the transmission system of the OFDM signal format is built, and the effectiveness of the proposed scheme has been confirmed.

关键词： autoencoder Signal Interference Elimination Integrated Sensing and Communication

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Color space-based autoencoder for optical camera communications

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Luna-Rivera, J. M. Rabadan, J. Rufo, J. Guerra, V. Moreno, D. Perez-Jimenez, R. Univ Autonoma San Luis Potosi Fac Sci San Luis Potosi Mexico Univ Las Palmas Gran Canaria IDeTIC Las Palmas Gran Canaria Spain Univ La Laguna ULL San Cristobal La Laguna Spain Pi Lighting Sarl Sion Switzerland

This paper proposes an end-to-end optical camera communications (OCC) system using an autoencoder neural network trained to recover the transmitted symbols. Although OCC techniques have been extensively studied in the literature, using an autoencoder that learns the transmitter and receiver functions jointly is a novel concept with significant prospects. Furthermore, we investigate the performance impact caused by the overlooked optical-to-electrical (O2E) conversion process of real-world OCC receivers. The autoencoder learning model captures these typically undesired changes in image sensors for the design of constellation symbols and reception schemes. For the simulation, we constructed an end-to-end autoencoder for a color space-based OCC system and measured the O2E performance effect. The proposed autoencoder communication system is analyzed and compared using the symbol error rate (SER) across various OCC detection systems. Despite the subtle spectral responsivity variations in image sensors, our numerical results indicate that the autoencoder model can learn to recover the transmitted data while minimizing SER and meeting the lighting requirements. These findings may interest a broad range of applications, particularly in IoT sensor networks. Among all the image sensors we studied, the OCC system with Bayer CFA-based signal detection showed superior performance.

关键词： vlc occ csk artifical intelligence autoencoder

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Integration of an autoencoder and background suppression for hyperspectral anomaly detection

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REMOTE SENSING LETTERS 2024年第9期15卷 977-987页

作者： Hu, Xing Chen, Tingting Zhang, Dawei Univ Shanghai Sci & Technol Sch Opt Elect Informat & Comp Engn 516 Jungong Rd Shanghai 200093 Peoples R China

The key to hyperspectral image (HSI) anomaly detection is effectively distinguishing anomalies from the background. Various detectors, such as low-rank sparse representation and background estimation, have been proposed. However, they usually exhibit poor performance due to complex backgrounds and similar anomalies and backgrounds. Considering that most of the pixels in HSI are background, the autoencoder can emphatically and automatically extract and reconstruct background features. And background suppression can further enhance the spectral differentiation of the anomalies and backgrounds. Therefore, we propose a new anomaly detector by integrating autoencoder and background suppression. First, the anomaly coefficient of HSI data is extracted via the local characteristics. The HSI data is fed into the 2Dimensional (2D) convolution autoencoder to reconstruct the background. Since the reconstruction background contains incorrect anomalies, the anomaly coefficient isused to suppress the reconstruction image. Then, the detection map is obtained with the Mahalanobis distance. The experiment is performed in different HSI datasets. The results illustrate that the proposed method is effective and outperforms other advanced methods in terms of detection performance.

关键词： Hyperspectral image anomaly detection autoencoder background suppression

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Detecting Nearshore Underwater Targets With Hyperspectral Nonlinear Unmixing autoencoder

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Liu, Jiaxuan Qi, Jiahao Zhu, Dehui Wen, Hao Jiang, Hejun Zhong, Ping Natl Univ Def Technol Coll Elect Sci & Technol Natl Key Lab Sci & Technol ATR Changsha 410073 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214000 Peoples R China

Hyperspectral underwater target detection (HUTD) is a promising and challenging task in remote sensing image processing. Existing methods face significant challenges when adapting to nearshore environments, where cluttered backgrounds hinder the extraction of target signatures and exacerbate signal distortion. Hyperspectral unmixing (HU) demonstrates potential effectiveness for nearshore underwater target detection (UTD) by simultaneously extracting water background endmembers and separating target signals. To this end, this article investigates a novel nonlinear unmixing network for hyperspectral UTD, denoted as nonlinear unmixing network for hyperspectral-UTD (NUN-UTD), in which a well-designed autoencoder-based unmixing network is used to obtain the abundance map as the detection result. To address the weak underwater target signals, a target prior spectral preservation scheme is employed to guide the unmixing network in learning the accurate target abundance. Besides, to address the complexity of the nearshore environment, a pseudomixed data classification constraint is incorporated into the objective function to enhance the discriminative capability between the background and the target. Moreover, we adopt an additive postnonlinear model in the decoder to deal with the interactions between underwater spectra to account for the nonlinear effects between spectra of underwater substances. To validate the effectiveness of the proposed method, we constructed a hyperspectral dataset for nearshore UTD. Extensive experiments conducted on three real-world datasets and one simulated dataset demonstrate that our method achieves outstanding performance in HUTD.

关键词： Hyperspectral imaging Object detection Decoding Rivers Feature extraction Adaptation models Training autoencoder hyperspectral image (HSI) nearshore underwater target detection (UTD) unmixing network weak target signals

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