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Machine and Deep Learning for Earth Observation Data Analysis

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 47872

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Special Issue Editors

Dr. Vasileios Syrris

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Guest Editor

European Commission, Joint Research Centre, I.3 Text and Data Mining, Directorate I-Competences, via E. Fermi 2749, I-21027 Ispra (VA), Italy
Interests: big data analysis; artificial/computational intelligence; machine learning; high-throughput/high-performance computing; automation; computer vision; remote sensing; robotics; control engineering; statistics

Mr. Sveinung Loekken

E-Mail Website
Guest Editor

European Space Agency, Directorate of Earth Observation Programmes, Future Systems Department, Phi-Lab, Largo Galileo Galilei 1, I-00044 Frascati (RM), Italy
Interests: big data analysis; artificial/computational intelligence; machine learning; remote sensing

Special Issue Information

Dear Colleagues,

Earth Observation and remote sensing technologies currently provide enormous volumes of data capable of supporting evidence-based science and informed policy-making. The heterogeneity, the frequency, and, most importantly, the magnitude of those collections of data call for robust and large-scale data analysis methods.

The aim of this Special Issue is to jointly present the new advancements on the topics of Earth Observation, big data, and automated data-driven modeling and analysis which are represented herein by machine and deep learning techniques. The proposed works should be built around and necessarily incorporate the concept of open science (open and free data, open code and software, shared resources, and so on) for precise reproducibility and present large-scale applications (in terms of data, modeling, and computational resources).

Scientists, scholars, and practitioners are invited to submit their original research papers on the following interrelated and not exclusive topics:

Remote sensing applications: land use, land cover, agricultural monitoring, emergency response, security, water resources observation and management, soil degradation, climate change, etc.;
Data: training sets for supervised learning, data fusion, sampling methods, granularity, reliability, and completeness;
Automated data analysis: large-scale machine learning, statistical learning, deep learning, transfer learning, data mining, feature engineering and high-dimensional spaces, parametrization, estimation, optimization;
Processing: real-time, incremental, CPU/GPU-based, data pre-processing, computing cluster, cloud and distributed processing;
Spatiotemporal processing: image filtering, morphological analysis, segmentation, tensor signal processing, time-series analysis, adaptive, local and global modeling.

Dr. Vasileios Syrris
Mr. Sveinung Loekken
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

Machine learning
Deep learning
Large-scale learning
Large-scale optimization
Automated knowledge extraction
Computational approaches to modelling, estimation, and inference
Big Data and data mining
Earth observation
Remote sensing applications
Computer vision
High-performance platforms and computing systems
Open Science

Published Papers (7 papers)

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Editorial

Jump to: Research

3 pages, 171 KiB

Open AccessEditorial

Editorial of Special Issue “Machine and Deep Learning for Earth Observation Data Analysis”

by Vasileios Syrris and Sveinung Loekken

Remote Sens. 2021, 13(14), 2758; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13142758 - 14 Jul 2021

Viewed by 1570

Abstract

Earth observation and remote sensing technologies provide ample and comprehensive information regarding the dynamics and complexity of the Earth system [...] Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

Research

Jump to: Editorial

16 pages, 2820 KiB

Open AccessArticle

Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

by Zheng Zhuo and Zhong Zhou

Remote Sens. 2021, 13(5), 869; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050869 - 26 Feb 2021

Cited by 14 | Viewed by 2519

Abstract

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

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Graphical abstract

22 pages, 34812 KiB

Open AccessArticle

Identifying Soil Erosion Processes in Alpine Grasslands on Aerial Imagery with a U-Net Convolutional Neural Network

by Maxim Samarin, Lauren Zweifel, Volker Roth and Christine Alewell

Remote Sens. 2020, 12(24), 4149; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12244149 - 18 Dec 2020

Cited by 12 | Viewed by 3443

Abstract

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25–0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F

_{1}

-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method suitable to efficiently and successfully capture the temporal trends and spatial heterogeneity of degradation in alpine grasslands. Additionally, U-Net is a powerful and robust tool to map erosion sites in a predictive manner utilising large amounts of new aerial imagery. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

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Graphical abstract

23 pages, 12681 KiB

Open AccessEditor’s ChoiceArticle

Application of Convolutional Neural Network for Spatiotemporal Bias Correction of Daily Satellite-Based Precipitation

by Xuan-Hien Le, Giha Lee, Kwansue Jung, Hyun-uk An, Seungsoo Lee and Younghun Jung

Remote Sens. 2020, 12(17), 2731; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172731 - 24 Aug 2020

Cited by 35 | Viewed by 6184

Abstract

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

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30 pages, 4805 KiB

Open AccessArticle

Fully Convolutional Neural Network for Rapid Flood Segmentation in Synthetic Aperture Radar Imagery

by Edoardo Nemni, Joseph Bullock, Samir Belabbes and Lars Bromley

Remote Sens. 2020, 12(16), 2532; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12162532 - 06 Aug 2020

Cited by 91 | Viewed by 20405

Abstract

Rapid response to natural hazards, such as floods, is essential to mitigate loss of life and the reduction of suffering. For emergency response teams, access to timely and accurate data is essential. Satellite imagery offers a rich source of information which can be analysed to help determine regions affected by a disaster. Much remote sensing flood analysis is semi-automated, with time consuming manual components requiring hours to complete. In this study, we present a fully automated approach to the rapid flood mapping currently carried out by many non-governmental, national and international organisations. We design a Convolutional Neural Network (CNN) based method which isolates the flooded pixels in freely available Copernicus Sentinel-1 Synthetic Aperture Radar (SAR) imagery, requiring no optical bands and minimal pre-processing. We test a variety of CNN architectures and train our models on flood masks generated using a combination of classical semi-automated techniques and extensive manual cleaning and visual inspection. Our methodology reduces the time required to develop a flood map by 80%, while achieving strong performance over a wide range of locations and environmental conditions. Given the open-source data and the minimal image cleaning required, this methodology can also be integrated into end-to-end pipelines for more timely and continuous flood monitoring. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

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Graphical abstract

28 pages, 4853 KiB

Open AccessArticle

Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation

by Aletta Dóra Schlosser, Gergely Szabó, László Bertalan, Zsolt Varga, Péter Enyedi and Szilárd Szabó

Remote Sens. 2020, 12(15), 2397; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12152397 - 26 Jul 2020

Cited by 22 | Viewed by 6581

Abstract

Urban sprawl related increase of built-in areas requires reliable monitoring methods and remote sensing can be an efficient technique. Aerial surveys, with high spatial resolution, provide detailed data for building monitoring, but archive images usually have only visible bands. We aimed to reveal the efficiency of visible orthophotographs and photogrammetric dense point clouds in building detection with segmentation-based machine learning (with five algorithms) using visible bands, texture information, and spectral and morphometric indices in different variable sets. Usually random forest (RF) had the best (99.8%) and partial least squares the worst overall accuracy (~60%). We found that >95% accuracy can be gained even in class level. Recursive feature elimination (RFE) was an efficient variable selection tool, its result with six variables was like when we applied all the available 31 variables. Morphometric indices had 82% producer’s and 85% user’s Accuracy (PA and UA, respectively) and combining them with spectral and texture indices, it had the largest contribution in the improvement. However, morphometric indices are not always available but by adding texture and spectral indices to red-green-blue (RGB) bands the PA improved with 12% and the UA with 6%. Building extraction from visual aerial surveys can be accurate, and archive images can be involved in the time series of a monitoring. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

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36 pages, 22784 KiB

Open AccessArticle

Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

by Stefan Bachhofner, Ana-Maria Loghin, Johannes Otepka, Norbert Pfeifer, Michael Hornacek, Andrea Siposova, Niklas Schmidinger, Kurt Hornik, Nikolaus Schiller, Olaf Kähler and Ronald Hochreiter

Remote Sens. 2020, 12(8), 1289; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081289 - 18 Apr 2020

Cited by 12 | Viewed by 4866

Abstract

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads. Full article

(This article belongs to the Special Issue Machine and Deep Learning for Earth Observation Data Analysis)

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