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Photovoltaic power station identification using refined encoder-decoder network with channel attention and chained residual dilated convolutions

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JOURNAL OF APPLIED REMOTE SENSING 2020年第1期14卷

作者： Jie, Yongshi Yue, Anzhi Liu, Shunxi Huang, Qingqing Chen, Jingbo Meng, Yu Deng, Yupeng Yu, Zongyang Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Land Surveying & Planning Inst Beijing Peoples R China Natl Engn Lab Transportat Safety & Emergency Info Beijing Peoples R China

The quantity and distribution of photovoltaic power stations play an important role in policymaking related to clean energy. High-resolution satellite imagery with a wide field of view enables photovoltaic power station monitoring at low cost. The separability varies greatly due to distinct backgrounds in different regions, which makes photovoltaic power station identification a challenging task. We propose an end-to-end semantic segmentation network to identify photovoltaic power stations under multiple complex backgrounds in Gaofen 1 imageries. Our network adopts the encoder-decoder architecture in which three modules between the encoder and decoder are added, i.e., feature refinement residual module (FRRM), chained dilation attention module (CDAM), and global channel attention module (GCAM). FRRM uses the structure of residual connection to refine the features from each stage of the encoder. CDAM consisting of chained residual dilated convolutions with different dilation rates and channel attention can enlarge the receptive field without reducing image resolution and select useful features. GCAM utilizes high-level features containing rich semantic information as a guide to select more discriminative low-level features. Experiments demonstrate the effectiveness of each module and the ability of the proposed network to improve photovoltaic power station identification under complex backgrounds. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： photovoltaic power station identification remote sensing semantic segmentation encoder-decoder channel attention

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Video Summarization With Attention-Based encoder-decoder Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第6期30卷 1709-1717页

作者： Ji, Zhong Xiong, Kailin Pang, Yanwei Li, Xuelong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning Sch Comp Sci Xian 710072 Peoples R China

This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, and the output is a keyshot sequence. Our key idea is to learn a deep summarization network with attention mechanism to mimic the way of selecting the keyshots of human. To this end, we propose a novel video summarization framework named attentive encoder-decoder networks for video summarization (AVS), in which the encoder uses a bidirectional long short-term memory (BiLSTM) to encode the contextual information among the input video frames. As for the decoder, two attention-based LSTM networks are explored by using additive and multiplicative objective functions, respectively. Extensive experiments are conducted on two video summarization benchmark datasets, i.e., SumMe and TVSum. The results demonstrate the superiority of the proposed AVS-based approaches against the state-of-the-art approaches, with remarkable improvements on both datasets.

关键词： Decoding Visualization Recurrent neural networks Additives Indexes Internet Semantics Video summarization LSTM encoder-decoder attention mechanism

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Using An Attention-Based LSTM encoder-decoder Network for Near Real-Time Disturbance Detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 1819-1832页

作者： Yuan, Yuan Lin, Lei Huo, Lian-Zhi Kong, Yun-Long Zhou, Zeng-Guang Wu, Bin Jia, Yan Nanjing Univ Posts & Telecommun Sch Geog & Biol Informat Nanjing 210023 Peoples R China Beijing Qihoo Technol Co Ltd Beijing 100015 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Aerosp DongFangHong Satellite Co Ltd Beijing 100094 Peoples R China

Accurate prediction of future observations based on past data is the key to near real-time disturbance detection using satellite image time series (SITS). To overcome the limitations of existing methods, we present an attention-based long-short-term memory (LSTM) encoder-decoder model in which the historical time series of a pixel is encoded with a bidirectional LSTM encoder while the future time series is produced by another LSTM decoder. An attention mechanism is integrated into the encoder-decoder model to align the input time series with the output time series and to dynamically choose the most relevant contextual information while forecasting. Based on the proposed model, we develop a framework for near real-time disturbance detection and verify its effectiveness in the case of burned area mapping. The prediction accuracy of the proposed model is evaluated using moderate resolution imaging spectroradiometer (MODIS) time series and compared with state-of-the-art models. Experimental results show that our model achieves the best results in terms of lower prediction error and higher model fitness. We also evaluate the disturbance detection ability of the proposed framework. The proposed approach improves the detection rate of disturbances while suppressing false alarms, and increases the temporal accuracy. We suggest that the proposed methods provide new tools for enhancing current early warning systems in real time.

关键词： Time series analysis Predictive models Hidden Markov models Real-time systems Remote sensing Forecasting Satellites Attention mechanism encoder-decoder long-short-term memory (LSTM) near real-time disturbance detection satellite image time series (SITS)

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Utilizing Attention-Based Multi-encoder-decoder Neural Networks for Freeway Traffic Speed Prediction

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11960-11969页

作者： Abdelraouf, Amr Abdel-Aty, Mohamed Yuan, Jinghui Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA

Speed prediction is a crucial yet complicated task for intelligent transportation systems. The challenge derives from the complex spatiotemporal dependencies of traffic parameters. In the past few years, deep neural networks have achieved the best traffic speed prediction performance. However, most models depend on short-term input sequences to predict short/long-term traffic speed (e.g., predicting speed for the next hour using data from the past hour). These models fail to consider the daily and weekly periodic behavior of traffic. Another problem posed by neural networks is the lack of interpretability as they often operate as ``black boxes''. In this paper, an attention-based multi-encoder-decoder (Att-MED) model is proposed to predict traffic speed. The model uses convolutional-LSTMs to capture the spatiotemporal relationship of multiple input sequences, namely short-term, daily and weekly traffic patterns. The model also employs an LSTM to model the output predictions sequentially. Furthermore, attention mechanism is used to weigh the contribution of each traffic sequence towards the output predictions. The proposed network architecture, when trained end-to-end, results in a superior prediction accuracy compared to baseline models. In addition to contributing towards performance, the attention mechanism creates weight values, which when visualized, provide insights into the decision-making process of the neural network, and consequently produce explainable outputs. Att-MED's extracted attention weights highlight the contribution of daily and weekly periodic input towards speed prediction.

关键词： Predictive models Feature extraction Roads Decoding Computational modeling Traffic control Data mining Traffic forecasting multi-sequence encoder-decoder attention explainable neural networks

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Multivariate time series forecasting via attention-based encoder-decoder framework

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NEUROCOMPUTING 2020年第0期388卷 269-279页

作者： Du, Shengdong Li, Tianrui Yang, Yan Horng, Shi-Jinn Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China Natl Taiwan Univ Sci & Technol Dept Comp Sci & Informat Engn Taipei 10607 Taiwan

Time series forecasting is an important technique to study the behavior of temporal data and forecast future values, which is widely applied in many fields, e.g. air quality forecasting, power load forecasting, medical monitoring, and intrusion detection. In this paper, we firstly propose a novel temporal attention encoder-decoder model to deal with the multivariate time series forecasting problem. It is an end-to-end deep learning structure that integrates the traditional encode context vector and temporal attention vector for jointly temporal representation learning, which is based on bi-directional long short-term memory networks (Bi-LSTM) layers with temporal attention mechanism as the encoder network to adaptively learning long-term dependency and hidden correlation features of multivariate temporal data. Extensive experimental results on five typical multivariate time series datasets showed that our model has the best forecasting performance compared with baseline methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multivariate time series Temporal attention Multi-step forecasting encoder-decoder Deep learning Long short-term memory networks

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An Improved and Robust encoder-decoder for Skin Lesion Segmentation

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第8期47卷 9861-9875页

作者： Hafhouf, Bellal Zitouni, Athmane Megherbi, Ahmed Chaouki Sbaa, Salim Univ Mohamed Khider Dept Elect Engn LESIA Lab Biskra Algeria Univ Mohamed Khider Dept Elect Engn LICCC Lab Biskra Algeria

Automatic segmentation of skin lesions is an important step in computer-aided diagnosis systems for melanoma detection. Although numerous methods have been proposed in the literature, this task is still a challenging issue due to the similarity between different lesions and complex visual characteristics that may be presented in the images. In this paper, we propose major modifications to the state-of-the-art U-Net structure to further improve its capability in skin lesion segmentation. These modifications are presented in both the encoding and the decoding paths. Instead of using only standard convolutional layers like U-Net, the proposed encoding path consists of 10 standard convolutional layers, which are inspired from the Visual Geometry Group (VGG16) network, followed by a pyramid pooling module and a dilated convolutional block. This combination enables to learn better representative feature maps and preserve more spatial resolution. Furthermore, dilated residual blocks are introduced in the decoding path to further refine the segmentation maps. The experimental results on three datasets including the IEEE International Symposium on Biomedical Imaging (ISBI) 2017, ISBI 2016, and PH2 showed that our proposed method has better performance than the basic U-Net, FCN, SegNet, and U-Net + + , and achieved the performance of state-of-the-art segmentation techniques, with minimum pre- and post-processing operations.

关键词： Dilated convolution Pyramid pooling Skin lesion segmentation Dermoscopy encoder-decoder U-Net

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River flood prediction through flow level modeling using multi-attention encoder-decoder-based TCN with filter-wrapper feature selection

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EARTH SCIENCE INFORMATICS 2024年第6期17卷 5233-5249页

作者： Jeba, G. Selva Chitra, P. Thiagarajar Coll Engn Dept Comp Sci & Engn Madurai India Thiagarajar Coll Engn Dept Comp Applicat Madurai India

Floods, among the most destructive climate-induced natural disasters, necessitate effective prediction models for early warning systems. The proposed Multi-Attention encoder-decoder-based Temporal Convolutional Network (MA-TCN-ED) prediction model combines the strengths of the Temporal Convolutional Network (TCN), Multi-Attention (MA) mechanism, and encoder-decoder (ED) architecture along with filter-wrapper feature selection for optimal feature selection. This framework aims to improve flood prediction accuracy by effectively capturing temporal dependencies and intricate patterns in atmospheric and hydro-meteorological data. The proposed framework was pervasively assessed for predicting the real-world flood-related data of the river Meenachil, Kerala, and the results showed that MA-TCN-ED using a filter-wrapper feature selection approach achieved higher accuracy in flood prediction. Further the model was validated on the dataset of river Pamba, Kerala. The proposed model exhibits better performance with about 32% reduced MAE, 39% reduced RMSE, 12% increased NSE, 14% enhanced R-2, and 17% enhanced accuracy relative to the average performance of all the compared baseline models. The proposed work holds promise for enhancing early warning systems and mitigating the impact of floods and contributes to the broader understanding of leveraging deep learning models for effective climate-related risk mitigation.

关键词： Environmental modeling Deep learning Flood prediction Attention mechanism encoder-decoder Feature selection

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Estimation of rolling bearing remaining useful life using DReLU-RA-ConvGRU model based on the encoder-decoder structure

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第3期35卷 036001-036001页

作者： Wu, Qile Xiao, Ke Xiong, Wanquan Han, Yanfeng Song, Bin Chongqing Univ State Key Lab Mech Transmiss Adv Equipments Chongqing 400044 Peoples R China ROKAE Beijing Technol Co Ltd Beijing 100080 Peoples R China

Rolling bearings serve as indispensable core components in modern industrial equipment and they are critical for safety and reliability. Consequently, accurate prediction of their remaining useful life (RUL) is essential and has far-reaching implications. This paper proposes a novel RUL prediction model, referred to as the dynamic rectified linear unit-based residual additive attention ConvGRU (DReLU-RA-ConvGRU) model, which is built upon the encoder-decoder structure to accurately predict the RUL of bearings. To overcome the limitation of the original signal, characterized by a single feature and limited degradation information, three-domain features are employed and filtered as inputs to the model. The DReLU component in the proposed RUL prediction model effectively captures variable feature information within the degraded signal, while the ConvGRU component learns both temporal and spatial information with fewer parameters. Additionally, the RA component captures the significant contributors to RUL prediction, and the inclusion of residuals facilitates easier network learning. Furthermore, a three-dimensional visualization of the attention weights was conducted to enhance the interpretability of the network's prediction process. In order to verify the effectiveness of the method, RUL prediction was conducted using vibration data from the PRONOSTIA platform and compared against several existing methods. The results demonstrate the method's superior performance and feasibility, as indicated by high scores.

关键词： rolling bearing encoder-decoder dynamic ReLU (DReLU) residual additive attention (RA) RUL prediction three-domain features

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A Gated Dilated Causal Convolution Based encoder-decoder for Network Traffic Forecasting

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IEEE ACCESS 2020年 8卷 6087-6097页

作者： Zhang, Xin You, Jiali Chinese Acad Sci Inst Acoust Natl Network New Media Engn Res Ctr Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

The accurate estimation of future network traffic is a key enabler for early warning of network degradation and automated orchestration of network resources. The long short-term memory neural network (LSTM) is a popular architecture for network traffic forecasting, and has been successfully used in many applications. However, it has been observed that LSTMs suffer from limited memory capacity problems when the sequence is long. In this paper, we propose a gated dilated causal convolution based encoder-decoder (GDCC-ED) model for network traffic forecasting. The GDCC-ED learns a vector representation in the encoder from historical network traffic series, in which gated dilated causal convolutions are adopted to expand the long-range memory capacity. Moreover, different types of features in various perspectives, including temporal-independent and temporal-related features, are incorporated. In the decoder, the GDCC-ED exploits an RNN with LSTM units to map the vector representation back to a variable-length target sequence. Besides, a sequence data augmentation technique is designed to solve the problem of data scarcity. Experimental results demonstrate that our model achieves superior performance than state-of-the-art algorithms by 11.6 & x0025;.

关键词： Network traffic forecasting dilated causal convolution gated activations encoder-decoder

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An encoder-decoder Convolution Network With Fine-Grained Spatial Information for Hyperspectral Images Classification

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IEEE ACCESS 2020年 8卷 33600-33608页

作者： Li, Zhongwei Guo, Fangming Li, Qi Ren, Guangbo Wang, Leiquan China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Minist Nat Resources Inst Oceanog 1 Qingdao 266061 Peoples R China China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China

Convolutional Neural Network (CNN) is widely used in Hyperspectral Images (HSIs) classification. However, the fine-grained spatial (FGS) details are discarded during a sequence of convolution and pooling operations for most of CNN-based HSIs classification methods. To address this issue, a unified encoder-decoder framework is proposed to integrate high-level semantics and FGS details for HSIs classification, denoted by FGSCNN. The encoder, including a series of convolution and pooling layers, captures the high-level semantic information with low resolution feature maps. The decoder fuses the high-level low-resolution semantic and the fine-grained high-resolution spatial information, namely, to get the FGS features with high-level semantics. The deconvolution layers and skip connection are used in the decoder to retain the FGS details, while, convolution layers are also used to combine the FGS features with high-level semantics. Based on the encoder-decoder framework, a unified loss function is exploited to integrate the high-level semantic information and FGS details with an end-to-end manner for HSIs classification. Experiments conducted on the three public datasets, i.e. the Indian Pines, Pavia University and Salinas, demonstrate the effectiveness of the proposed method on HSIs classification.

关键词： Semantics Feature extraction Convolution Decoding Spatial resolution Hyperspectral imaging Machine learning Convolutional neural networks (CNNs) encoder-decoder hyperspectral image (HSI) classification information fusion

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