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Current Limits and New Challenges and Opportunities in Soft Computing, Machine Learning and Computational Intelligence for Remote Sensing

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 21825

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Special Issue Editors

Prof. Dr. Jorge Garcia-Gutierrez

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Guest Editor

Department of Computer Science Languages and Systems, University of Seville, E.T.S.I.I., Reina Mercedes s/n, 41012 Sevilla, Spain
Interests: data streams; data engineering; remote sensing; medical informatics; soft computing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Maria del Mar Martinez-Ballesteros

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Guest Editor

ETSII, University of Seville, Av. Reina Mercedes s/n, 41012 Seville, Spain
Interests: association rules; big data; evolutionary computation; data mining; data science; metaheuristics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing (RS) has been an inexhaustible source of data in which the most innovative techniques from the world of computational intelligence have been applied. The different studies carried out in recent years show great synergies between both disciplines. Despite the good results, however, the limitations of computational techniques in the context of RS should be delimited to make new proposals that could increase the quality of the RS products.

All the above is more important, if possible, bearing in mind that remote sensors are being used in areas that would originally be outside the traditional scope of RS, such as autonomous vehicles or real-time monitoring for security. These new opportunities involve the need to develop novel methodologies that adapt the general RS framework (including management of data with high variety, velocity, and volume).

This Special Issue thus focuses on the exploration of novel ideas regarding disciplines related to computational intelligence in RS. We call for contributions that describe novel methods and ongoing research, including algorithm development, training strategies, and implementations, but also studies to find out the real limitations of current computational techniques in RS. We specifically invite you to submit the most recent advances in the following (or related) topics:

Change and target detection in single- and multitemporal analysis;
Deep, transfer, and active learning from single and multiple data sources;
Large-scale RS data analysis;
Machine learning and pattern recognition methodologies for RS;
Multimodal data fusion;
Near-real time and real-time processing;
Semantic and image segmentation.

Prof. Dr. Jorge Garcia-Gutierrez
Dr. Maria del Mar Martinez-Ballesteros
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

Big Data
Change detection and monitoring
Data fusion and preprocessing
Deep learning
Feature learning and representation
Image processing and classification
Machine learning in LiDAR, radar, and other active sensors
Metaheuristics in RS
Novel RS applications
Object detection and image segmentation
Quality assessment for products and models in RS
Satellite imagery
Real-time applications
Reinforcement learning
Vegetation monitoring

Published Papers (4 papers)

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Research

29 pages, 8191 KiB

Open AccessArticle

Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network

by Munkhjargal Gochoo, Israr Akhter, Ahmad Jalal and Kibum Kim

Remote Sens. 2021, 13(5), 912; https://doi.org/10.3390/rs13050912 - 28 Feb 2021

Cited by 49 | Viewed by 2848

Abstract

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods. Full article

(This article belongs to the Special Issue Current Limits and New Challenges and Opportunities in Soft Computing, Machine Learning and Computational Intelligence for Remote Sensing)

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23 pages, 5360 KiB

Open AccessArticle

On the Performance of One-Stage and Two-Stage Object Detectors in Autonomous Vehicles Using Camera Data

by Manuel Carranza-García, Jesús Torres-Mateo, Pedro Lara-Benítez and Jorge García-Gutiérrez

Remote Sens. 2021, 13(1), 89; https://doi.org/10.3390/rs13010089 - 29 Dec 2020

Cited by 98 | Viewed by 11813

Abstract

Object detection using remote sensing data is a key task of the perception systems of self-driving vehicles. While many generic deep learning architectures have been proposed for this problem, there is little guidance on their suitability when using them in a particular scenario such as autonomous driving. In this work, we aim to assess the performance of existing 2D detection systems on a multi-class problem (vehicles, pedestrians, and cyclists) with images obtained from the on-board camera sensors of a car. We evaluate several one-stage (RetinaNet, FCOS, and YOLOv3) and two-stage (Faster R-CNN) deep learning meta-architectures under different image resolutions and feature extractors (ResNet, ResNeXt, Res2Net, DarkNet, and MobileNet). These models are trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context. For the experimental study, we use the Waymo Open Dataset, which is the largest existing benchmark. Despite the rising popularity of one-stage detectors, our findings show that two-stage detectors still provide the most robust performance. Faster R-CNN models outperform one-stage detectors in accuracy, being also more reliable in the detection of minority classes. Faster R-CNN Res2Net-101 achieves the best speed/accuracy tradeoff but needs lower resolution images to reach real-time speed. Furthermore, the anchor-free FCOS detector is a slightly faster alternative to RetinaNet, with similar precision and lower memory usage. Full article

(This article belongs to the Special Issue Current Limits and New Challenges and Opportunities in Soft Computing, Machine Learning and Computational Intelligence for Remote Sensing)

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18 pages, 2993 KiB

Open AccessArticle

Machine Learning for Cloud Detection of Globally Distributed Sentinel-2 Images

by Roberto Cilli, Alfonso Monaco, Nicola Amoroso, Andrea Tateo, Sabina Tangaro and Roberto Bellotti

Remote Sens. 2020, 12(15), 2355; https://doi.org/10.3390/rs12152355 - 22 Jul 2020

Cited by 20 | Viewed by 3854

Abstract

In recent years, a number of different procedures have been proposed for segmentation of remote sensing images, basing on spectral information. Model-based and machine learning strategies have been investigated in several studies. This work presents a comprehensive overview and an unbiased comparison of the most adopted segmentation strategies: Support Vector Machines (SVM), Random Forests, Neural networks, Sen2Cor, FMask and MAJA. We used a training set for learning and two different independent sets for testing. The comparison accounted for 135 images acquired from 54 different worldwide sites. We observed that machine learning segmentations are extremely reliable when the training and test are homogeneous. SVM performed slightly better than other methods. In particular, when using heterogeneous test data, SVM remained the most accurate segmentation method while state-of-the-art model-based methods such as MAJA and FMask obtained better sensitivity and precision, respectively. Therefore, even if each method has its specific advantages and drawbacks, SVM resulted in a competitive option for remote sensing applications. Full article

(This article belongs to the Special Issue Current Limits and New Challenges and Opportunities in Soft Computing, Machine Learning and Computational Intelligence for Remote Sensing)

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Graphical abstract

27 pages, 1432 KiB

Open AccessArticle

A Soft Computing Approach for Selecting and Combining Spectral Bands

by Juan F. H. Albarracín, Rafael S. Oliveira, Marina Hirota, Jefersson A. dos Santos and Ricardo da S. Torres

Remote Sens. 2020, 12(14), 2267; https://doi.org/10.3390/rs12142267 - 15 Jul 2020

Cited by 5 | Viewed by 2570

Abstract

We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes. Full article

(This article belongs to the Special Issue Current Limits and New Challenges and Opportunities in Soft Computing, Machine Learning and Computational Intelligence for Remote Sensing)

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