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Remote Sensing
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Statistical and Machine Learning Models for Remote Sensing Data Mining...
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Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 15706

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Dr. Monidipa Das

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Guest Editor
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
Interests: spatio-temporal data mining; spatial informatics; autonomous machine learning; streaming data analytics
Dr. Soumya K. Ghosh

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Guest Editor
Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
Interests: spatial informatics; spatial web services; cloud computing; machine learning
Dr. V. M. Chowdary

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Guest Editor
National Remote Sensing Centre, Indian Space Research Organization, Delhi, India
Interests: application of geospatial technologies; multi-criteria analysis and soft computing tools for agricultural water management; integrated watershed management; hydrological modeling; land use/cover changes
Dr. Pabitra Mitra

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Guest Editor
Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
Interests: machine learning; data mining; information retrieval
Dr. Santosh Rijal

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Guest Editor
Department of Geography, Virginia Tech, VA 24061, USA
Interests: application of geographic information science (GIScience) in multiple disciplines; modeling and prediction of land condition; spatial analysis; ecosystem services

Special Issue Information

Dear Colleagues,

It is our great pleasure to organize a Special Issue of Remote Sensing titled “Statistical and Machine Learning Models for Remote Sensing Data Mining—Recent Advancements”.

In the past few decades, we have witnessed remarkable progressions in the field of satellite and remote sensing technologies, which have enabled us to capture high-resolution hyperspectral earth imagery on weekly, daily, and even hourly bases. This wealth of earth surface data, if analyzed effectively, can provide significant insights into various geo-spatial processes and, eventually, can help us make crucial decisions in a timely manner. However, these remotely sensed data, as continuously captured at varying spatial, spectral, and temporal resolutions, are not only gigantic, but also heterogeneous because they are generated by diverse categories of sensors using different optical/microwave technologies. Consequently, mining useful patterns/information from these enormous volumes of heterogeneous unstructured data requires enhanced data mining techniques exploiting the power of advanced computational intelligence and high-performance distributed computing paradigms. Moreover, in the context of resolving urgent issues, such as in environmental nowcasting, a timely analysis of the remote sensing data requires resource-efficient computation models with real-time processing and online analysis capabilities.

With this background in mind, in this Special Issue, we call for high-quality papers focusing on recent advancements in purely statistical as well as machine learning techniques and modern AI (artificial intelligence)-driven technologies for efficient mining of remote sensing data. The main topics of this Special Issue include the following: 1) identification of challenges and opportunities for traditional AI-driven approaches and machine learning models to mine huge volumes of available remote sensing data and 2) moving towards the development of advanced computational frameworks and learning paradigms for resource-efficient computation that can support online as well as real-time analytics of remote sensing data. This Special Issue also aims to provide a common platform for professionals, researchers, and practitioners from heterogeneous communities, including artificial intelligence, machine learning, GIS (geographical information systems), and spatial data science, to share their views, innovations, research achievements, and solutions to foster the advancement of intelligent analytics and efficient management of remote sensing data. Major topics of interest include, but are not limited to, the following:

  • Advanced machine learning models for remote sensing data mining:
    • Autonomous learning model;
    • Supervised learning model;
    • Unsupervised learning model;
    • Reinforcement learning model;
    • Transfer learning model;
    • Deep learning
  • Enhanced statistical models for remote sensing data mining:
    • Theory-guided data-driven models;
    • Semantically enhanced
  • Intelligent techniques for efficient storage and management of remote sensing data;
  • Energy-efficient processing and analysis of remote sensing data;
  • High-performance and scalable computing methods for mining remote sensing data;
  • Real-time processing and online analytics of remote sensing data;
  • Real-world applications of remote sensing data mining:
    • Climate change pattern analysis;
    • Urban sprawl detection;
    • Land-cover classification;
    • Water quality monitoring;
    • Disaster monitoring;
    • Environmental damage assessment;
    • Hydrological sciences;
    • Agricultural applications and so

Please feel free to disseminate this announcement to any of your colleagues who might be interested.

Dr. Monidipa Das
Dr. Soumya K. Ghosh
Dr. V. M. Chowdary
Dr. Pabitra Mitra
Dr. Santosh Rijal
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
  • machine learning
  • enhanced statistical model
  • autonomous learning
  • resource-efficient computing
  • real-time data analytics
  • data mining
  • online learning

Published Papers (6 papers)

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Editorial

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3 pages, 168 KiB  
Editorial
Statistical and Machine Learning Models for Remote Sensing Data Mining—Recent Advancements
by Monidipa Das, Soumya K. Ghosh, Vemuri M. Chowdary, Pabitra Mitra and Santosh Rijal
Remote Sens. 2022, 14(8), 1906; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081906 - 15 Apr 2022
Cited by 2 | Viewed by 1552
Abstract
During the last few decades, the remarkable progress in the field of satellite remote sensing (RS) technology has enabled us to capture coarse, moderate to high-resolution earth imagery on weekly, daily, and even hourly intervals [...] Full article
(This article belongs to the Special Issue Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements)

Research

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16 pages, 3827 KiB  
Article
High Wind Speed Inversion Model of CYGNSS Sea Surface Data Based on Machine Learning
by Yun Zhang, Jiwei Yin, Shuhu Yang, Wanting Meng, Yanling Han and Ziyu Yan
Remote Sens. 2021, 13(16), 3324; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163324 - 23 Aug 2021
Cited by 11 | Viewed by 2515
Abstract
In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at [...] Read more.
In response to the deficiency of the detection capability of traditional remote sensing means (scatterometer, microwave radiometer, etc.) for high wind speed above 25 m/s, this paper proposes a GNSS-R technique combined with a machine learning method to invert high wind speed at sea surface. The L1-level satellite-based data from the Cyclone Global Navigation Satellite System (CYGNSS), together with the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) data, constitute the original sample set, which is processed and trained with Support Vector Regression (SVR), the combination of Principal Component Analysis (PCA) and SVR (PCA-SVR), and Convolutional Neural Network (CNN) methods, respectively, to finally construct a sea surface high wind speed inversion model. The three models for high wind speed inversion are certified by the test data collected during Typhoon Bavi in 2020. The results show that all three machine learning models can be used for high wind speed inversion on sea surface, among which the CNN method has the highest inversion accuracy with a mean absolute error of 2.71 m/s and a root mean square error of 3.80 m/s. The experimental results largely meet the operational requirements for high wind speed inversion accuracy. Full article
(This article belongs to the Special Issue Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements)
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20 pages, 4935 KiB  
Article
Oil Spill Detection in SAR Images Using Online Extended Variational Learning of Dirichlet Process Mixtures of Gamma Distributions
by Ahmed Almulihi, Fahd Alharithi, Sami Bourouis, Roobaea Alroobaea, Yogesh Pawar and Nizar Bouguila
Remote Sens. 2021, 13(15), 2991; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13152991 - 29 Jul 2021
Cited by 18 | Viewed by 2379
Abstract
In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case. In particular, we propose a novel online nonparametric Bayesian analysis method based on the infinite [...] Read more.
In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case. In particular, we propose a novel online nonparametric Bayesian analysis method based on the infinite Gamma mixture model where the determination of the number of clusters is bypassed via an infinite number of mixture components. The proposed model is learned via an online extended variational Bayesian inference approach in a flexible way where the priors of model’s parameters are selected appropriately and the posteriors are approximated effectively in a closed form. The online setting has the advantage to allow data instances to be treated in a sequential manner, which is more attractive than batch learning especially when dealing with massive and streaming data. We demonstrated the performance and merits of the proposed statistical framework with a challenging real-world application namely oil spill detection in synthetic aperture radar (SAR) images. Full article
(This article belongs to the Special Issue Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements)
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24 pages, 10596 KiB  
Article
Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model
by Yufeng Chi, Zhifeng Wu, Kuo Liao and Yin Ren
Remote Sens. 2020, 12(22), 3786; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12223786 - 18 Nov 2020
Cited by 9 | Viewed by 2684
Abstract
Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on [...] Read more.
Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite’s 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants. Full article
(This article belongs to the Special Issue Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements)
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21 pages, 7525 KiB  
Article
Remote Sensing Image Scene Classification with Noisy Label Distillation
by Rui Zhang, Zhenghao Chen, Sanxing Zhang, Fei Song, Gang Zhang, Quancheng Zhou and Tao Lei
Remote Sens. 2020, 12(15), 2376; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152376 - 24 Jul 2020
Cited by 15 | Viewed by 3335
Abstract
The widespread applications of remote sensing image scene classification-based Convolutional Neural Networks (CNNs) are severely affected by the lack of large-scale datasets with clean annotations. Data crawled from the Internet or other sources allows for the most rapid expansion of existing datasets at [...] Read more.
The widespread applications of remote sensing image scene classification-based Convolutional Neural Networks (CNNs) are severely affected by the lack of large-scale datasets with clean annotations. Data crawled from the Internet or other sources allows for the most rapid expansion of existing datasets at a low-cost. However, directly training on such an expanded dataset can lead to network overfitting to noisy labels. Traditional methods typically divide this noisy dataset into multiple parts. Each part fine-tunes the network separately to improve performance further. These approaches are inefficient and sometimes even hurt performance. To address these problems, this study proposes a novel noisy label distillation method (NLD) based on the end-to-end teacher-student framework. First, unlike general knowledge distillation methods, NLD does not require pre-training on clean or noisy data. Second, NLD effectively distills knowledge from labels across a full range of noise levels for better performance. In addition, NLD can benefit from a fully clean dataset as a model distillation method to improve the student classifier’s performance. NLD is evaluated on three remote sensing image datasets, including UC Merced Land-use, NWPU-RESISC45, AID, in which a variety of noise patterns and noise amounts are injected. Experimental results show that NLD outperforms widely used directly fine-tuning methods and remote sensing pseudo-labeling methods. Full article
(This article belongs to the Special Issue Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements)
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Other

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16 pages, 4106 KiB  
Technical Note
Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields
by Yingying Kong, Biyuan Yan, Yanjuan Liu, Henry Leung and Xiangyang Peng
Remote Sens. 2021, 13(7), 1323; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071323 - 30 Mar 2021
Cited by 4 | Viewed by 2301
Abstract
In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such [...] Read more.
In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such as water bodies. One of the current challenges is how to fuse the benefits of both to obtain more powerful classification capabilities. This study proposes a classification model based on random forest with the conditional random fields (CRF) for feature-level fusion classification using features extracted from polarized SAR and optical images. In this paper, feature importance is introduced as a weight in the pairwise potential function of the CRF to improve the correction rate of misclassified points. The results show that the dataset combining the two provides significant improvements in feature identification when compared to the dataset using optical or polarized SAR image features alone. Among the four classification models used, the random forest-importance_ conditional random fields (RF-Im_CRF) model developed in this paper obtained the best overall accuracy (OA) and Kappa coefficient, validating the effectiveness of the method. Full article
(This article belongs to the Special Issue Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements)
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