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Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images

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

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 16502

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Dr. Peng Liu

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: explainable artificial intelligence; active deep learning; remote sensing image fusion
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Guojin He

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Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: intelligent processing of remote sensing big data; remote sensing data based Ready To Use (RTU) product; remote sensing information engineering and application
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Kie B. Eom

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Electrical & Computer Engineering, George Washington University, Science & Engineering Hall, Suite 2885, 800 22nd Street, NW, Washington, DC 20052, USA
Interests: electrical engineering; signal and image processing; systems and controls

Prof. Dr. Mohd Anul Haq

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Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Majmaah 11952, Saudi Arabia
Interests: deep Learning; geospatial modeling

Special Issue Information

Dear Colleagues,

With the fast development of remote sensing technology, simultaneous consideration of time and space characteristics has become one of the most effective ways to acquire information of the Earth's surface. Spatiotemporal patterns can be considered the change rules in a dynamic system. It is becoming progressively important with the increasing availability of large spatiotemporal datasets such as maps, satellite remote sensing, aerial remote sensing, and GPS trajectories. Mining valuable knowledge from spatiotemporal data is a fundamental problem in many remote sensing applications, including land cover change, ecological monitoring, smart transportation, city development, environmental protection, etc.

Traditional data mining techniques often perform poorly when applied to large spatiotemporal datasets because of their continuous and independent assumption. Furthermore, traditional data mining methods overly depend on handcraft feature engineering, especially for long-time sequence data or multi-source remote sensing images. Therefore, the performance of model-driven methods is limited in analyzing the complex characteristics of spatiotemporal data. In recent years, powerful computing, large training datasets, and advanced AI algorithms have achieved considerable development. With the prevalence of deep learning, data-driven methods are becoming the mainstream trend in spatiotemporal pattern-mining of remote sensing data. New architectures such as GNN, RNN, LSTM, and Transformer, among other, show promising performances in spatiotemporal pattern-mining. With automatic feature learning and powerful generalization ability, these data-driven methods make better use of the information of spatiotemporal data and provide both higher accuracy and reliability in data mining. Data-driven methods also provide new opportunities to understand the spatiotemporal pattern in many remote sensing applications.

With these issues in mind, it is time to present the current state-of-the-art theoretical, methodological, and application research on data-driven methods for spatiotemporal pattern-mining. This Special Issue invites articles that are related to the topic of remote sensing and geospatial big data analytics. We welcome high-quality contributions proposing solutions and approaches in the domain of, but not limited to, the following topics:

New data-driven methods for spatiotemporal data, such as GNN, transformer, etc.;
Spatiotemporal pattern-mining for forest, water, etc.;
Spatiotemporal pattern-mining for environment and ecosystem features;
Spatiotemporal pattern-mining for urban expansion;
Change detection with remote sensing images;
Spatial–temporal fusion of remote sensing images;
Dynamical monitoring on marine ranching; and
Image quality evaluation with spatiotemporal big data.

Dr. Peng Liu
Prof. Dr. Guojin He
Prof. Dr. Kie B. Eom
Prof. Dr. Mohd Anul Haq
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

spatial–temporal fusion
data-driven methods
spatiotemporal big data
dynamical monitoring
environment and ecosystem

Published Papers (8 papers)

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Research

24 pages, 17889 KiB

Open AccessArticle

In-Memory Distributed Mosaicking for Large-Scale Remote Sensing Applications with Geo-Gridded Data Staging on Alluxio

by Yan Ma, Jie Song and Zhixin Zhang

Remote Sens. 2022, 14(23), 5987; https://doi.org/10.3390/rs14235987 - 25 Nov 2022

Cited by 4 | Viewed by 1558

Abstract

The unprecedented availability of petascale analysis-ready earth observation data has given rise to a remarkable surge in demand for regional to global environmental studies, which exploit tons of data for temporal–spatial analysis at a much larger scale than ever. Imagery mosaicking, which is critical for forming “One Map” with a continuous view for large-scale climate research, has drawn significant concern. However, despite employing distributed data processing engines such as Spark, large-scale data mosaicking still significantly suffers from a staggering number of remote sensing images which could inevitably lead to discouraging performance. The main ill-posed problem of traditional parallel mosaicking algorithms is inherent in the huge computation demand and incredible heavy data I/O burden resulting from intensively shifting tremendous RS data back and forth between limited local memory and bulk external storage throughout the multiple processing stages. To address these issues, we propose an in-memory Spark-enabled distributed data mosaicking at a large scale with geo-gridded data staging accelerated by Alluxio. It organizes enormous “messy” remote sensing datasets into geo-encoded gird groups and indexes them with multi-dimensional space-filling curves geo-encoding assisted by GeoTrellis. All the buckets of geo-grided remote sensing data groups could be loaded directly from Alluxio with data prefetching and expressed as RDDs implemented concurrently as grid tasks of mosaicking on top of the Spark-enabled cluster. It is worth noticing that an in-memory data orchestration is offered to facilitate in-memory big data staging among multiple mosaicking processing stages to eliminate the tremendous data transferring at a great extent while maintaining a better data locality. As a result, benefiting from parallel processing with distributed data prefetching and in-memory data staging, this is a much more effective approach to facilitate large-scale data mosaicking in the context of big data. Experimental results have demonstrated our approach is much more efficient and scalable than the traditional ways of parallel implementing. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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19 pages, 5681 KiB

Open AccessArticle

Remote Sensing Image Super-Resolution via Residual-Dense Hybrid Attention Network

by Bo Yu, Bin Lei, Jiayi Guo, Jiande Sun, Shengtao Li and Guangshuai Xie

Remote Sens. 2022, 14(22), 5780; https://doi.org/10.3390/rs14225780 - 16 Nov 2022

Cited by 1 | Viewed by 1548

Abstract

Nowadays, remote sensing datasets with long temporal coverage generally have a limited spatial resolution, most of the existing research uses the single image super-resolution (SISR) method to reconstruct high-resolution (HR) images. However, due to the lack of information in low-resolution (LR) images and the ill-posed nature of SISR, it is difficult to reconstruct the fine texture of HR images under large-scale magnification factors (e.g., four times). To address this problem, we propose a new reference-based super-resolution method called a Residual-Dense Hybrid Attention Network (R-DHAN), which uses the rich texture information in the reference image to make up for the deficiency of the original LR image. The proposed SR model employs Super-Resolution by Neural Texture Transfer (SRNTT) as a backbone. Based on this structure, we propose a dense hybrid attention block (DHAB) as a building block of R-DHAN. The DHAB fuses the input and its internal features of current block. While making full use of the feature information, it uses the interdependence between different channels and different spatial dimensions to model and obtains a strong representation ability. In addition, a hybrid channel-spatial attention mechanism is introduced to focus on important and useful regions to better reconstruct the final image. Experiments show that compared with SRNTT and some classical SR techniques, the proposed R-DHAN method performs well in quantitative evaluation and visual quality. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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19 pages, 21263 KiB

Open AccessArticle

Time-Series FY4A Datasets for Super-Resolution Benchmarking of Meteorological Satellite Images

by Jingbo Wei, Chenghao Zhou, Jingsong Wang and Zhou Chen

Remote Sens. 2022, 14(21), 5594; https://doi.org/10.3390/rs14215594 - 06 Nov 2022

Cited by 1 | Viewed by 1579

Abstract

Meteorological satellites are usually operated at high temporal resolutions, but the spatial resolutions are too poor to identify ground content. Super-resolution is an economic way to enhance spatial details, but the feasibility is not validated for meteorological images due to the absence of benchmarking data. In this work, we propose the FY4ASRgray and FY4ASRcolor datasets to assess super-resolution algorithms on meteorological applications. The features of cloud sensitivity and temporal continuity are linked to the proposed datasets. To test the usability of the new datasets, five state-of-the-art super-resolution algorithms are gathered for contest. Shift learning is used to shorten the training time and improve the parameters. Methods are modified to deal with the 16-bit challenge. The reconstruction results are demonstrated and evaluated regarding the radiometric, structural, and spectral loss, which gives the baseline performance for detail enhancement of the FY4A satellite images. Additional experiments are made on FY4ASRcolor for sequence super-resolution, spatiotemporal fusion, and generalization test for further performance test. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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25 pages, 15284 KiB

Open AccessArticle

RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery

by Juan Tian, Daifeng Peng, Haiyan Guan and Haiyong Ding

Remote Sens. 2022, 14(18), 4527; https://doi.org/10.3390/rs14184527 - 10 Sep 2022

Cited by 4 | Viewed by 1853

Abstract

Change detection (CD) methods work on the basis of co-registered multi-temporal images with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit period, it is difficult to acquire the desired images, especially in emergency situations. In addition, accurate multi-temporal images co-registration is largely limited by vast object changes and matching algorithms. To this end, a resolution- and alignment-aware change detection network (RACDNet) is proposed for multi-resolution optical remote-sensing imagery CD. In the first stage, to generate high-quality bi-temporal images, a light-weighted super-resolution network is proposed by fully considering the construction difficulty of different regions, which facilitates to detailed information recovery. Adversarial loss and perceptual loss are further adopted to improve the visual quality. In the second stage, deformable convolution units are embedded in a novel Siamese–UNet architecture for bi-temporal deep features alignment; thus, robust difference features can be generated for change information extraction. We further use an atrous convolution module to enlarge the receptive field, and an attention module to bridge the semantic gap between the encoder and decoder. To verify the effectiveness of our RACDNet, a novel multi-resolution change detection dataset (MRCDD) is created by using Google Earth. The quantitative and qualitative experimental results demonstrate that our RACDNet is capable of enhancing the details of the reconstructed images significantly, and the performance of CD surpasses other state-of-the-art methods by a large margin. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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25 pages, 9999 KiB

Open AccessArticle

CD_HIEFNet: Cloud Detection Network Using Haze Optimized Transformation Index and Edge Feature for Optical Remote Sensing Imagery

by Qing Guo, Lianzi Tong, Xudong Yao, Yewei Wu and Guangtong Wan

Remote Sens. 2022, 14(15), 3701; https://doi.org/10.3390/rs14153701 - 02 Aug 2022

Cited by 3 | Viewed by 3143

Abstract

Clouds in optical remote sensing images are an unavoidable existence that greatly affect the utilization of these images. Therefore, accurate and effective cloud detection is an indispensable step in image preprocessing. To date, most researchers have tried to use deep-learning methods for cloud detection. However, these studies generally use computer vision technology to improve the performances of the models, without considering the unique spectral feature information in remote sensing images. Moreover, due to the complex and changeable shapes of clouds, accurate cloud-edge detection is also a difficult problem. In order to solve these problems, we propose a deep-learning cloud detection network that uses the haze-optimized transformation (HOT) index and the edge feature extraction module for optical remote sensing images (CD_HIEFNet). In our model, the HOT index feature image is used to add the unique spectral feature information from clouds into the network for accurate detection, and the edge feature extraction (EFE) module is employed to refine cloud edges. In addition, we use ConvNeXt as the backbone network, and we improved the decoder to enhance the details of the detection results. We validated CD_HIEFNet using the Landsat-8 (L8) Biome dataset and compared it with the Fmask, FCN8s, U-Net, SegNet, DeepLabv3+ and CloudNet methods. The experimental results showed that our model has excellent performance, even in complex cloud scenarios. Moreover, according to the extended experimental results for the other L8 dataset and the Gaofen-1 data, CD_HIEFNet has strong performance in terms of robustness and generalization, thus helping to provide new ideas for cloud detection-related work. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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18 pages, 12038 KiB

Open AccessArticle

PoSDMS: A Mining System for Oceanic Dynamics with Time Series of Raster-Formatted Datasets

by Lianwei Li, Cunjin Xue, Yangfeng Xu, Chengbin Wu and Chaoran Niu

Remote Sens. 2022, 14(13), 2991; https://doi.org/10.3390/rs14132991 - 22 Jun 2022

Viewed by 1135

Abstract

Many effective and advanced methods have been developed to explore oceanic dynamics using time series of raster-formatted datasets; however, they have generally been designed at a scale suitable for data observation and used independently of each other, despite the potential advantages of combining different modules into an integrated system at a scale suited for dynamic evolution. From raster-formatted datasets to marine knowledge, we developed and integrated several mining algorithms at a dynamic evolutionary scale and combined them into six modules: a module of raster-formatted dataset pretreatment; a module of process-oriented object extraction; a module of process-oriented representation and management (process-oriented graph database); a module of process-oriented clustering; a module of process-oriented association rule mining; and a module of process-oriented visualization. On the basis of such modules, we developed a process-oriented spatiotemporal dynamic mining system named PoSDMS (Process-oriented Spatiotemporal Dynamics Mining System). PoSDMS was designed to have the capacity to deal with at least six environments of marine anomalies with 40 years of raster-formatted datasets, including their extraction, representation, storage, clustering, association and visualization. The effectiveness of the integrated system was evaluated in a case study of sea surface temperature datasets during the period from January 1982 to December 2021 in global oceans. The main contribution of this study was the development of a mining system at a scale suited for dynamic evolution, providing an analyzing platform or tool to deal with time series of raster-formatted datasets to aid in obtaining marine knowledge. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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18 pages, 7848 KiB

Open AccessArticle

Remote Prediction of Oilseed Rape Yield via Gaofen-1 Images and a Crop Model

by Wenchao Tang, Rongxin Tang, Tao Guo and Jingbo Wei

Remote Sens. 2022, 14(9), 2041; https://doi.org/10.3390/rs14092041 - 24 Apr 2022

Cited by 5 | Viewed by 1814

Abstract

The fast and accurate prediction of crop yield at the regional scale is of great significance to food policies or trade. In this study, a new model is developed to predict the yield of oilseed rape from high-resolution remote sensing images. In order to derive this model, the ground experiment and remote sensing data analysis are carried out successively. In the ground experiment, the leaf area index (LAI) of four growing stages are measured, and a regression model is established to predict yield from ground LAI. In the remote sensing analysis, a new model is built to predict ground LAI from Gaofen-1 images where the simple ratio vegetation index at the bolting stage and the VARIgreen vegetation index at the flowering stage are used. The WOFOSTWOrld FOod STudy (WOFOST) crop model is used to generate time-series ground LAI from discontinuous ground LAI, which is calibrated coarsely with the MODerate resolution imaging spectroradiometer LAI product and finely with the ground-measured data. By combining the two conclusive formulas, an estimation model is built from Gaofen-1 images to the yield of oilseed rape. The effectiveness of the proposed model is verified in Wuxue City, Hubei Province from 2014 to 2019, with the pyramid bottleneck residual network to extract oilseed rape planting areas, the proposed model to estimate yields, and the China statistical yearbooks for comparison. The validation shows that the prediction error of the proposed algorithm is less than 5.5%, which highlights the feasibility of our method for accurate prediction of the oilseed rape yield in a large area. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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28 pages, 11788 KiB

Open AccessArticle

A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images

by Wenbo Zhao, Qing Dong and Zhengli Zuo

Remote Sens. 2022, 14(6), 1367; https://doi.org/10.3390/rs14061367 - 11 Mar 2022

Cited by 16 | Viewed by 2587

Abstract

Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection. Full article

(This article belongs to the Special Issue Data-Driven Methods for Spatiotemporal Pattern Mining of Remote Sensing Images)

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