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Advancing soil property prediction with encoder-decoder structures integrating traditional deep learning methods in Vis-NIR spectroscopy

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GEODERMA 2024年 449卷

作者： Ke, Ziyi Ren, Shilin Yin, Liang Beijing Univ Chem Technol Coll Math & Phys Beijing 100029 Peoples R China Beijing Key Lab Environmentally Harmful Chem Anal Beijing 100029 Peoples R China

The technology for estimating soil properties using visible and near-infrared spectroscopy has been maturing, with corresponding advances and breakthroughs in deep learning models. In this study, based on the large soil spectral library LUCAS, we explore the potential of encoder-decoder structures to improve convolutional neural network regression predictions. By introducing an encoder-decoder structure into the feature channels of a sixlayer CNN model (TRNN model), we significantly enhanced the performance of shallow CNN models and successfully carried out regression predictions for seven soil properties. We employed IntegratedGradients, DeepLift, GradientShap, and DeepLiftShap methods to interpret the output of the TRNN model. Our TRNN model, built on raw spectra, demonstrated high accuracy in predicting multiple soil properties, outperforming residual architectures, LSTMs, various CNN architectures, and other traditional machine learning methods proposed in previous studies. We also investigated the impact of multi-task output structures (TRNN 1-M and TRNN M-M) and single-task output structures (TRNN 1-1) on model performance. For the TRNN model with an encoder-decoder structure, multi-task output structures resulted in a reduction in performance. The TRNN showed outstanding results in regression analysis of the seven soil properties selected in this study (cation exchange capacity, organic carbon content, calcium carbonate content, pH, clay content, silt content, and sand content), with R2 values exceeding 0.93 for all seven properties. Different soil characteristics correspond to different wavelengths, with multiple characteristic peaks commonly observed. This research convincingly demonstrates the enormous potential of combining large model architectures with traditional deep learning approaches for predicting soil properties, which could significantly advance precision agriculture.

关键词： Deep learning encoder-decoder Feature wavelengths LUCAS topsoil dataset Soil properties

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Band-Independent encoder-decoder Network for Pan-Sharpening of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2020年第7期58卷 5208-5223页

作者： Liu, Chi Zhang, Yongjun Wang, Shugen Sun, Mingwei Ou, Yangjun Wan, Yi Liu, Xiu Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China

Pan-sharpening is a fundamental task for remote sensing image processing. It aims at creating a high-resolution multispectral (HRMS) image from a multispectral (MS) image and a panchromatic (PAN) image. In this article, a new band-independent encoder-decoder network is proposed for pan-sharpening. The network takes a single band of the MS (BMS) image, the PAN image, and the low-resolution PAN (LRPAN) image as inputs. The output of the network is the corresponding band of high-resolution MS (HRBMS) image. In this way, the network can process MS images with any number of bands. The overall structure of the network consists of two encoder-decoder modules at low-resolution and high-resolution, respectively. An auxiliary LRPAN image is used to speed up the training and improve the performance. The partly shared network and hierarchical structure for low-resolution and high-resolution enable a better fusion of features extracted from different scales. With a fast fine-tuning strategy, the trained model can be applied to images from different sensors. Experiments performed on different data sets demonstrate that the proposed method outperforms several state-of-the-art pan-sharpening methods in both visual appearance and objective indexes, and the single-band evaluation results further verify the superiority of the proposed method.

关键词： Decoding Image resolution Task analysis Remote sensing Feature extraction Sensors Transforms Band-independent deep learning encoder-decoder pan-sharpening

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An encoder-decoder model with embedded attention-mechanism for efficient meshfree prediction of slope failure

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2023年第10期32卷 1164-1187页

作者： Chen, Jun Wang, Dongdong Deng, Like Ying, Jijun Xiamen Univ Inst Artificial Intelligence Dept Civil Engn Fujian Key Lab Digital Simulat Coastal Civil Engn Xiamen Peoples R China Xiamen Univ Dept Civil Engn Xiamen 361005 Fujian Peoples R China

The particle-based meshfree methods provide an effective means for large deformation simulation of the slope failure. Despite the advances of various efficient meshfree algorithmic developments, the computational efficiency still limits the application of meshfree methods for practical problems. This study aims at accelerating the meshfree prediction of the slope failure through introducing an encoder-decoder model, which is particularly enhanced by the attention-mechanism. The encoder-decoder model is designed to capture the long sequence character of meshfree slope failure analysis. The discretization flexibility of meshfree methods offers an easy match between the meshfree particles and machine learning samples and thus the resulting surrogate model for meshfree slope failure prediction has a quite wide applicability. In the meantime, the embedding of the attention-mechanism into the encoder-decoder neural network not only enables a significant reduction of the number of meshfree model parameters, but also maintains the key features of meshfree simulation and effectively alleviates the information dilution issue. It is shown that the proposed encoder-decoder model with embedded attention mechanism gives a more favorable prediction on the meshfree slope failure simulation in comparison to the general encoder-decoder formalism.

关键词： Meshfree method large deformation slope failure encoder-decoder attention mechanism

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Self-Attention (SA)-ConvLSTM encoder-decoder Structure-Based Video Prediction for Dynamic Motion Estimation

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APPLIED SCIENCES-BASEL 2024年第23期14卷 11315页

作者： Kim, Jeongdae Choo, Hyunseung Jeong, Jongpil Sungkyunkwan Univ Dept AI Syst Engn 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 Gyeonggi Do South Korea

Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spatiotemporal prediction, which is an important decision-making tool in various fields, several deep learning models have been proposed. Convolutional long short-term memory (ConvLSTM) can capture space and time simultaneously and has shown excellent performance in various applications, such as image and video prediction, object detection, and semantic segmentation. However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder-decoder structure using self-attention ConvLSTM (SA-ConvLSTM), which retains the advantages of ConvLSTM and effectively captures the long-range dependencies through the self-attention mechanism. The effectiveness of the encoder-decoder structure using SA-ConvLSTM was validated through experiments on the MovingMNIST, KTH dataset.

关键词： SA-ConvLSTM encoder-decoder video prediction spatiotemporal self-attention memory module

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Deep Quasi-Recurrent Self-Attention With Dual encoder-decoder in Biomedical CT Image Segmentation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第12期28卷 7195-7205页

作者： Agarwal, Rohit Chowdhury, Arindam Chatterjee, Rajib Kumar Chel, Haradhan Murmu, Chiranjib Murmu, Narayan Nandi, Debashis Natl Inst Technol Dept Comp Sci & Engn Durgapur 713209 India Cent Inst Technol Dept Elect & Commun Engn Kokrajhar 783370 India Diamond Harbour Govt Med Coll Diamond Harbour 743331 India

Developing deep learning models for accurate segmentation of biomedical CT images is challenging due to their complex structures, anatomy variations, noise, and unavailability of sufficient labeled data to train the models. There are many models in the literature, but the researchers are yet to be satisfied with their performance in analyzing biomedical Computed Tomography (CT) images. In this article, we pioneer a deep quasi-recurrent self-attention structure that works with a dual encoder-decoder. The proposed novel deep quasi-recurrent self-attention architecture evokes parameter reuse capability that offers consistency in learning and quick convergence of the model. Furthermore, the quasi-recurrent structure leverages the features acquired from the previous time points and elevates the segmentation quality. The model also efficiently addresses long-range dependencies through a selective focus on contextual information and hierarchical representation. Moreover, the dynamic and adaptive operation, incremental and efficient information processing of the deep quasi-recurrent self-attention structure leads to improved generalization across different scales and levels of abstraction. Along with the model, we innovate a new training strategy that fits with the proposed deep quasi-recurrent self-attention architecture. The model performance is evaluated on various publicly available CT scan datasets and compared with state-of-the-art models. The result shows that the proposed model outperforms them in segmentation quality and training speed. The model can assist physicians in improving the accuracy of medical diagnoses.

关键词： deep learning encoder-decoder Biomedical CT image segmentation self-attention self-attention U-Net U-Net self-attention U-Net

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Real-time assessment on health state for bearing based on parallel encoder-decoder observer

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第5期40卷 2276-2291页

作者： Li, Kunpeng Mi, Jinhua Wang, Zhiguo Yin, Shengjie Bai, Libing Qiu, Gen Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu Peoples R China Shanghai Space Prop Technol Res Inst Dept SRM Design Shanghai Peoples R China

Bearings are foundational supporting components in diverse mechanical systems, essential for the reliable operation of these systems through real-time monitoring and precise health state assessment. However, vibration signals from bearings in practical equipment often contain excessive noise and redundant information, complicating health state assessment. To address this challenge, this paper proposes a neural network-based method named parallel encoder-decoder (PED). This method features a parallel architecture that combines the long short-term memory network and the temporal convolutional network for the encoder, along with a self-attention module for the decoder. PED is adept at learning the temporal representations hidden in original signals and filtering vibration signals to remove noise and redundant information. Additionally, a multi-objective loss function is developed to enhance the prediction results. A normalized Mahalanobis distance-based metric is then employed to compare residual signals during bearing operation with those under normal conditions. The case study evaluates the PED observer's proficiency in accurately predicting vibration signals and assessing the performance of health indicator curves, demonstrating the proposed PED observer's superiority over conventional networks.

关键词： bearing health state assessment encoder-decoder Mahalanobis distance multi-objective loss function

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A Convolutional encoder-decoder Network With Skip Connections for Saliency Prediction

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IEEE ACCESS 2019年 7卷 60428-60438页

作者： Qi, Fei Lin, Chunhuan Shi, Guangming Li, Hao Xidian Univ Sch Artificial Intelligence Xian 710071 Shaanxi Peoples R China

In this paper, we propose a novel convolutional encoder-decoder network with skip connections, named CEDNS, to improve the performance of saliency prediction. The encoder network utilizes the DenseNet model as the stem network to extract abundant hierarchical features from input images. Subsequently, a decoder network is designed to sufficiently fuse the hierarchical features to predict saliency more accurately. Between the encoder and decoder, skip connections are employed to transfer hierarchical features produced by the former to the latter. Furthermore, the model can be trained in an end-to-end manner which is beneficial for both training and inference. The experimental results on various benchmark datasets, SALICON, MIT300, and CAT2000, show that the proposed model achieves state-of-the-art performance on several key metrics.

关键词： Saliency convolutional neural networks encoder-decoder skip connections visual attention

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An automated detection model of threat objects for X-Ray baggage inspection based on modified encoder-decoder model

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第8期39卷 2730-2755页

作者： Sara, Dioline Mandava, Ajay Kumar GITAM Sch Technol Dept Elect Elect & Commun Engn Bangalore Campus Bengaluru India

Modern security faces challenges in detecting unauthorized and potentially harmful items in luggage, despite X-ray baggage scanning frameworks and research on efficiently screening highly disguised items. In response to this gap, a groundbreaking Modified encoder-decoder-based model has been introduced. This innovative model takes X-ray scan images as input and generates distinct feature representations for both suspicious and non-suspicious baggage materials. A key focus of the model is to address the denoising challenge inherent in X-ray images which reduces the models efficiency. This is achieved through the implementation of a Poisson Noise Reduction method during the preprocessing stage. Following preprocessing, the model effectively segments the non-threat image, identifying potential threats from the denoised input. The model showcases superior performance, as evidenced by high Peak Signal-to-Noise Ratio (PSNR) and low Mean Squared Error (MSE) values, outperforming existing filtering techniques. Rigorous testing on publicly available SIXray and GDXray datasets validates the effectiveness of the proposed methodology. Performance metrics for the SIXray dataset, including mAP, IoU, and DC values of 97.32%, 73.14%, and 85.12%, respectively, underscore the model's efficacy. Notably, the framework attains an impressive accuracy of 99.17% on the SIXray dataset, affirming its robustness in addressing contemporary security challenges.

关键词： X-ray images baggage screening encoder-decoder wavelet transform U-Net SVM classifier

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LGI-rPPG-Net: A shallow encoder-decoder model for rPPG signal estimation from facial video streams

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 89卷

作者： Chowdhury, Moajjem Hossain Chowdhury, Muhammad E. H. Reaz, Mamun Bin Ibne Ali, Sawal Hamid Md Rakhtala, Seyed Mehdi Murugappan, M. Mahmud, Sakib Shuzan, Nazmul Islam Bakar, Ahmad Ashrif A. Abd Razak, Mohd Ibrahim Bin Shapiai Khan, Muhammad Salman Khandakar, Amith Univ Kebangsaan Malaysia Ctr Adv Elect & Commun Engn Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Qatar Univ Dept Elect Engn 2713 Doha Qatar Independent Univ Dept Elect & Elect Engn Dhaka Bangladesh Univ Teknol Malaysia Malaysia Japan Int Inst Technol Kuala Lumpur 54100 Malaysia Golestan Univ Fac Engn Dept Elect Engn Gorgan Iran Kuwait Coll Sci & Technol Dept Elect & Commun Engn Intelligent Signal Proc ISP Res Lab Block 4 Doha 13133 Kuwait

A method to accurately estimate physiological signals from video streams at a minimal cost is invaluable. The importance of such a technique in pre-clinical health monitoring cannot be understated. Remote photoplethysmography (rPPG) can be used as a substitute for finger photoplethysmography (PPG) when such sensors are not recommended, such as for burn victims, premature babies, and patients with sensitive skin. Good quality rPPG signal that is highly correlated to finger PPG can be used to estimate many vital health signs. In this work, a shallow encoder-decoder architecture, LGI-rPPG-Net is proposed. The proposed model aims to produce highly correlated rPPG signals which can be substituted for finger PPG. In the reconstruction of rPPG, the model achieved a very good Pearson's Correlation Coefficient (PCC), Root Mean Squared Error (RMSE), and dynamic time warping distance of 0.862, 0.148, and 0.699, respectively. This highly correlated rPPG was compared to finger PPG by calculating heart rate from rPPG and finger PPG. The model achieved a PCC of 0.984 and RMSE, and MAE of 2.91, 1.51 beats per minute (BPM), respectively. LGI-rPPG-Net model with video streaming to predict rPPG can thus be used as a replacement for finger PPG where in-contact collection is not feasible.

关键词： Remote Photoplethysmography (rPPG) Heart Rate (HR) Convolutional Neural Network (CNN) encoder-decoder Remote Health Monitoring

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Graph Regularized encoder-decoder Networks for Image Representation Learning

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 3124-3136页

作者： Yang, Shijie Li, Liang Wang, Shuhui Zhang, Weigang Huang, Qingming Tian, Qi Univ Chinese Acad Sci UCAS Sch Comp Sci & Technol Beijing 101408 Peoples R China UCAS Key Lab Big Data Min & Knowledge Management Beijing 101408 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Weihai 264209 Peoples R China Chinese Acad Sci Univ Chinese Acad Sci Beijing 100049 Peoples R China Huawei Noahs Ark Lab Hong Kong Peoples R China

Image representation learning with encoder-decoder networks plays a fundamental role in multimedia processing. Recent findings show that traditional encoder-decoders can be negatively affected by small visual perturbations. The learned non-smooth feature embedding cannot guarantee to capture semantic-meaningful geometric distance between visually-similar image samples. Inspired by manifold learning, we propose a graph regularized encoder-decoder network, which can preserve local geometric information of the code embedding space. More discriminative feature embedding is learnt to attain both high-level image semantic and neighbor relationship of image clusters. The proposed graph regularizer is formulated upon multi-layer perceptions. It uses the local invariance principle to explicitly reconstruct the geometric similarity graph. Theoretical analysis is provided to show the connection between our deep regularizer and traditional graph Laplacian regularizer. Practically, the network complexity is alleviated by anchor based bipartite graph, and this leverages our method into large scale scenario. Experimental evaluations show the comparable results of the proposed method with state-of-the-art models on different tasks.

关键词： Laplace equations Visualization Manifolds Image reconstruction Task analysis Decoding Semantics Auto-encoder encoder-decoder graph regularizer image representation learning

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