A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series
Environmental images that are captured by satellites can provide significant information for weather forecasting, climate warning, and so on. This article introduces a novel deep neural network that integrates a convolutional attention feature extractor (CAFE) in a recurrent neural network frame and...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/14/3552 |
| _version_ | 1827731868819128320 |
|---|---|
| author | Chao Li Haoran Wang Qinglei Su Chunlin Ning Teng Li |
| author_facet | Chao Li Haoran Wang Qinglei Su Chunlin Ning Teng Li |
| author_sort | Chao Li |
| collection | DOAJ |
| description | Environmental images that are captured by satellites can provide significant information for weather forecasting, climate warning, and so on. This article introduces a novel deep neural network that integrates a convolutional attention feature extractor (CAFE) in a recurrent neural network frame and a multivariate dynamic time warping (MDTW) loss. The CAFE module is designed to capture the complicated and hidden dependencies within image series between the source variable and the target variable. The proposed method can reconstruct the image series across environmental variables. The performance of the proposed method is validated by experiments using a real-world remote sensing dataset and compared with several representative methods. Experimental results demonstrate the emerging performance of the proposed method for cross-variable image series reconstruction. |
| first_indexed | 2024-03-11T00:40:47Z |
| format | Article |
| id | doaj.art-2c4c0d3c83b14ad5a4b414fdc90f6c5c |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T00:40:47Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2c4c0d3c83b14ad5a4b414fdc90f6c5c2023-11-18T21:12:24ZengMDPI AGRemote Sensing2072-42922023-07-011514355210.3390/rs15143552A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image SeriesChao Li0Haoran Wang1Qinglei Su2Chunlin Ning3Teng Li4First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaSchool of Control Science and Engineering, Shandong University, Jinan 250061, ChinaEnvironmental images that are captured by satellites can provide significant information for weather forecasting, climate warning, and so on. This article introduces a novel deep neural network that integrates a convolutional attention feature extractor (CAFE) in a recurrent neural network frame and a multivariate dynamic time warping (MDTW) loss. The CAFE module is designed to capture the complicated and hidden dependencies within image series between the source variable and the target variable. The proposed method can reconstruct the image series across environmental variables. The performance of the proposed method is validated by experiments using a real-world remote sensing dataset and compared with several representative methods. Experimental results demonstrate the emerging performance of the proposed method for cross-variable image series reconstruction.https://www.mdpi.com/2072-4292/15/14/3552image seriesfield reconstructionremote sensingenvironmental monitoring |
| spellingShingle | Chao Li Haoran Wang Qinglei Su Chunlin Ning Teng Li A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series Remote Sensing image series field reconstruction remote sensing environmental monitoring |
| title | A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series |
| title_full | A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series |
| title_fullStr | A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series |
| title_full_unstemmed | A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series |
| title_short | A Convolution and Attention Neural Network with MDTW Loss for Cross-Variable Reconstruction of Remote Sensing Image Series |
| title_sort | convolution and attention neural network with mdtw loss for cross variable reconstruction of remote sensing image series |
| topic | image series field reconstruction remote sensing environmental monitoring |
| url | https://www.mdpi.com/2072-4292/15/14/3552 |
| work_keys_str_mv | AT chaoli aconvolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT haoranwang aconvolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT qingleisu aconvolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT chunlinning aconvolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT tengli aconvolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT chaoli convolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT haoranwang convolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT qingleisu convolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT chunlinning convolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries AT tengli convolutionandattentionneuralnetworkwithmdtwlossforcrossvariablereconstructionofremotesensingimageseries |