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Intelligent Processing, Mining and Application of Remote Sensing Information

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6902

Special Issue Editors


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Guest Editor
Aerospace Information Research Institute (AIR), Chinese Academy of Sciences, Beijing 100094, China
Interests: Intelligent processing and application of remote sensing information; forest-burned area remote sensing mapping; nighttime light remote sensing
Special Issues, Collections and Topics in MDPI journals
China Institute of Water Resources and Hydropower Research, Beijing100038, China
Interests: remote sensing information mining; large-scale surface water system mapping; night light remote sensing; flood disaster remote sensing

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Guest Editor
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
Interests: remote sensing image processing and analysis; coastal remote sensing; coastal surveying and mapping; ocean remote sensing

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Guest Editor
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Private Bag 3, Wits 2050, Johannesburg, South Africa
Interests: hyperspectral and multispectral remote sensing; GIS modelling for environmental and agriculture applications
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Civil, Urban, Earth, and Environmental Engineering, UNIST (Ulsan National Institute of Science and Technology), Ulsan, Republic of Korea
Interests: satellite remote sensing; aerosols; air quality; wild fire; urban heatwave; drought; artificial intelligence; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technology has revolutionized our ability to gather valuable information about the Earth’s surface and atmosphere from a distance. The continuous advancements in sensor technology and data acquisition platforms have led to an unprecedented surge in the collection of remote sensing data. To derive meaningful insights and knowledge from this vast amount of data, it has become imperative to employ intelligent processing, mining, and application techniques.

Ongoing research efforts are primarily focused on the development of advanced algorithms, the integration of multi-source data, and the application of remote sensing in various domains such as environmental monitoring, agriculture, and disaster management. The need for intelligent remote sensing techniques arises from the escalating availability and complexity of remote sensing data, as well as the growing demand for accurate and timely information to support decision making across diverse sectors. The advancements in this field have the potential to revolutionize our understanding and management of the Earth's resources and environment.

This Special Issue aims to highlight the latest advancements and innovative approaches in the field of intelligent processing, mining, and applications of remote sensing information. We welcome original research articles, reviews, and case studies that delve into various aspects related to the intelligent analysis, interpretation, and utilization of remote sensing data. By showcasing cutting-edge research in this area, we hope to contribute to the ongoing progress and dissemination of knowledge in the field of remote sensing technology.

Dr. Tengfei Long
Dr. Wei Jiang
Dr. Xing Wang
Dr. Elhadi Adam
Prof. Dr. Jungho Im
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing image processing
  • artificial intelligence
  • fusion of multi-sensor remote sensing data
  • nighttime light remote sensing
  • sustainable development goals
  • disaster and natural resource monitoring
  • socioeconomic remote sensing

Published Papers (5 papers)

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Research

24 pages, 9207 KiB  
Article
SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides
by Laidian Xi, Junchuan Yu, Daqing Ge, Yunxuan Pang, Ping Zhou, Changhong Hou, Yichuan Li, Yangyang Chen and Yuanbiao Dong
Remote Sens. 2024, 16(13), 2334; https://doi.org/10.3390/rs16132334 - 26 Jun 2024
Viewed by 726
Abstract
Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently [...] Read more.
Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently proposed segment anything model (SAM) has shown strong generalization capabilities in zero-shot semantic segmentation. Nevertheless, SAM heavily relies on user-provided prompts, and performs poorly in identifying landslides on remote sensing images. In this study, we propose a SAM-based cross-feature fusion network (SAM-CFFNet) for the landslide identification task. The model utilizes SAM’s image encoder to extract multi-level features and our proposed cross-feature fusion decoder (CFFD) to generate high-precision segmentation results. The CFFD enhances landslide information through fine-tuning and cross-fusing multi-level features while leveraging a shallow feature extractor (SFE) to supplement texture details and improve recognition performance. SAM-CFFNet achieves high-precision landslide identification without the need for prompts while retaining SAM’s robust feature extraction capabilities. Experimental results on three open-source landslide datasets show that SAM-CFFNet outperformed other comparative models in terms of landslide identification accuracy and achieved an intersection over union (IoU) of 77.13%, 55.26%, and 73.87% on the three datasets, respectively. Our ablation studies confirm the effectiveness of each module designed in our model. Moreover, we validated the justification for our CFFD design through comparative analysis with diverse decoders. SAM-CFFNet achieves precise landslide identification using remote sensing images, demonstrating the potential application of the SAM-based model in geohazard analysis. Full article
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17 pages, 8120 KiB  
Article
A Daily High-Resolution Sea Surface Temperature Reconstruction Using an I-DINCAE and DNN Model Based on FY-3C Thermal Infrared Data
by Zukun Li, Daoming Wei, Xuefeng Zhang, Yaoting Gao and Dianjun Zhang
Remote Sens. 2024, 16(10), 1745; https://doi.org/10.3390/rs16101745 - 15 May 2024
Viewed by 523
Abstract
The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as [...] Read more.
The sea surface temperature (SST) is one of the most important parameters that characterize the thermal state of the ocean surface, directly affecting the heat exchange between the ocean and the atmosphere, climate change, and weather generation. Generally, due to factors such as the weather, satellite scanning orbit range, and satellite sensor malfunction, there are large areas of missing satellite remote sensing SST data, greatly reducing data utilization. In this situation, how to use effective data or avenues to rebuild missing SST data has become a research hotspot in the field of ocean remote sensing. Based on the SST data from an FY-3C visible and infrared radiometer with a spatial resolution of 5 km (FY-3C VIRR), an improved data interpolation convolutional autoencoder (I-DINCAE) was used to reconstruct the missing SST data. Through cross-validation, the accuracy of the reconstruction results was quantitatively evaluated with an RMSE of 0.36 °C and an MAE of 0.24 °C. The results showed that the I-DINCAE algorithm outperformed the original DINCAE algorithm greatly. For further optimization, a deep neural network (DNN) was chosen to adjust the error between the reconstructed SST and the in situ data. The RMSE of the final adjusted SST and in situ data is 0.466 °C, and the MAE is 0.296 °C. Compared to the in situ data, the accuracy of the adjusted data has shown a significant improvement over the reconstructed data. This method successfully applies deep-learning technology to the reconstruction of SST data, achieving the full coverage and high accuracy of SST products, which can provide more reliable and complete SST data for marine scientific research. Full article
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20 pages, 5103 KiB  
Article
County-Level Poverty Evaluation Using Machine Learning, Nighttime Light, and Geospatial Data
by Xiaoqian Zheng, Wenjiang Zhang, Hui Deng and Houxi Zhang
Remote Sens. 2024, 16(6), 962; https://doi.org/10.3390/rs16060962 - 9 Mar 2024
Viewed by 1190
Abstract
The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment [...] Read more.
The accurate and timely acquisition of poverty information within a specific region is crucial for formulating effective development policies. Nighttime light (NL) remote sensing data and geospatial information provide the means for conducting precise and timely evaluations of poverty levels. However, current assessment methods predominantly rely on NL data, and the potential of combining multi-source geospatial data for poverty identification remains underexplored. Therefore, we propose an approach that assesses poverty based on both NL and geospatial data using machine learning models. This study uses the multidimensional poverty index (MPI), derived from county-level statistical data with social, economic, and environmental dimensions, as an indicator to assess poverty levels. We extracted a total of 17 independent variables from NL and geospatial data. Machine learning models (random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM)) and traditional linear regression (LR) were used to model the relationship between the MPI and independent variables. The results indicate that the RF model achieved significantly higher accuracy, with a coefficient of determination (R2) of 0.928, a mean absolute error (MAE) of 0.030, and a root mean square error (RMSE) of 0.037. The top five most important variables comprise two (NL_MAX and NL_MIN) from the NL data and three (POI_Ed, POI_Me, and POI_Ca) from the geographical spatial data, highlighting the significant roles of NL data and geographical data in MPI modeling. The MPI map that was generated by the RF model depicted the detailed spatial distribution of poverty in Fujian province. This study presents an approach to county-level poverty evaluation that integrates NL and geospatial data using a machine learning model, which can contribute to a more reliable and efficient estimate of poverty. Full article
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26 pages, 25329 KiB  
Article
A Hybrid Algorithm with Swin Transformer and Convolution for Cloud Detection
by Chengjuan Gong, Tengfei Long, Ranyu Yin, Weili Jiao and Guizhou Wang
Remote Sens. 2023, 15(21), 5264; https://doi.org/10.3390/rs15215264 - 6 Nov 2023
Cited by 7 | Viewed by 2041
Abstract
Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, [...] Read more.
Cloud detection is critical in remote sensing image processing, and convolutional neural networks (CNNs) have significantly advanced this field. However, traditional CNNs primarily focus on extracting local features, which can be challenging for cloud detection due to the variability in the size, shape, and boundaries of clouds. To address this limitation, we propose a hybrid Swin transformer–CNN cloud detection (STCCD) network that combines the strengths of both architectures. The STCCD network employs a novel dual-stream encoder that integrates Swin transformer and CNN blocks. Swin transformers can capture global context features more effectively than traditional CNNs, while CNNs excel at extracting local features. The two streams are fused via a fusion coupling module (FCM) to produce a richer representation of the input image. To further enhance the network’s ability in extracting cloud features, we incorporate a feature fusion module based on the attention mechanism (FFMAM) and an aggregation multiscale feature module (AMSFM). The FFMAM selectively merges global and local features based on their importance, while the AMSFM aggregates feature maps from different spatial scales to obtain a more comprehensive representation of the cloud mask. We evaluated the STCCD network on three challenging cloud detection datasets (GF1-WHU, SPARCS, and AIR-CD), as well as the L8-Biome dataset to assess its generalization capability. The results show that the STCCD network outperformed other state-of-the-art methods on all datasets. Notably, the STCCD model, trained on only four bands (visible and near-infrared) of the GF1-WHU dataset, outperformed the official Landsat-8 Fmask algorithm in the L8-Biome dataset, which uses additional bands (shortwave infrared, cirrus, and thermal). Full article
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22 pages, 8751 KiB  
Article
Field Patch Extraction Based on High-Resolution Imaging and U2-Net++ Convolutional Neural Networks
by Chen Long, Song Wenlong, Sun Tao, Lu Yizhu, Jiang Wei, Liu Jun, Liu Hongjie, Feng Tianshi, Gui Rongjie, Haider Abbas, Meng Lingwei, Lin Shengjie and He Qian
Remote Sens. 2023, 15(20), 4900; https://doi.org/10.3390/rs15204900 - 10 Oct 2023
Cited by 2 | Viewed by 1353
Abstract
Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland [...] Read more.
Accurate extraction of farmland boundaries is crucial for improving the efficiency of farmland surveys, achieving precise agricultural management, enhancing farmers’ production conditions, protecting the ecological environment, and promoting local economic development. Remote sensing and deep learning are feasible methods for creating large-scale farmland boundary maps. However, existing neural network models have limitations that restrict the accuracy and reliability of agricultural parcel extraction using remote sensing technology. In this study, we used high-resolution satellite images (2 m, 1 m, and 0.8 m) and the U2-Net++ model based on the RSU module, deep separable convolution, and the channel-spatial attention mechanism module to extract different types of fields. Our model exhibited significant improvements in farmland parcel extraction compared with the other models. It achieved an F1-score of 97.13%, which is a 7.36% to 17.63% improvement over older models such as U-Net and FCN and a more than 2% improvement over advanced models such as DeepLabv3+ and U2-Net. These results indicate that U2-Net++ holds the potential for widespread application in the production of large-scale farmland boundary maps. Full article
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