Special Issue "Computational Intelligence and Advanced Learning Techniques in Remote Sensing"

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

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Edoardo Pasolli
E-Mail Website
Guest Editor
Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Portici, Naples, Italy
Interests: multi/hyperspectral remote sensing; image processing and analysis; machine learning; pattern recognition; computer vision
Special Issues and Collections in MDPI journals
Dr. Zhou Zhang
E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Wisconsin-Madison, 230 Agricultural Engineering Building, 460 Henry Mall, Madison, WI 53706
Interests: Hyperspectral remote sensing; machine learning; unmanned aerial vehicle (UAV)-based imaging platform developments; precision agriculture; high-throughput plant phenotyping
Special Issues and Collections in MDPI journals
Dr. Zhengxia Zou
E-Mail Website
Guest Editor
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, North Campus Research Complex Building 300, Ann Arbor, MI 48105, USA
Interests: Remote sensing image processing and analysis; computer vision; machine learning; pattern recognition
Dr. ZhiYong Lv
E-Mail Website
Guest Editor
School of computer science and Engineering, Xi’An University of Technology, Jin Hua South Road No.5, Xi’An City, Shaan Xi Province, China, 710054
Interests: Very high-resolution remote sensing images; land cover change detection; landslide inventory mapping; land cover classification and pattern recognition; remote sensing application; machine learning

Special Issue Information

Dear Colleagues,

In the last couple of decades, remote sensing has represented a fundamental technology to monitor urban and natural areas at local and global scales. Important achievements have been obtained thanks to the growing availability of sensors having improved spatial and spectral resolutions and placed on different platforms such as satellite, airborne, and newly developed UAVs systems.

Such improvements in terms of acquisition capabilities open at the same time relevant challenges in terms of processing methodologies. More specifically, traditional image analysis techniques are impractical and ineffective to extract meaningful information from the growing amount of collected data. New strategies from both the methodological and the computational sides are required to deal with this massive amount of data.

In this Special Issue, we welcome methodological contributions in terms of innovative computational intelligence and learning techniques as well as the application of advanced methodologies to relevant scenarios from remote sensing data. We invite you to submit the most recent advancements in the following, and related, topics:

  • Machine learning and pattern recognition methodologies for remote sensing image analysis
  • Deep, transfer, and active learning from single and multiple sources
  • Semantic and image segmentation
  • Manifold learning
  • Large-scale image analysis
  • Change and target detection in single- and multi-temporal analysis
  • Multi-modal data fusion
  • Near-real time and real-time processing

Dr. Edoardo Pasolli
Dr. Zhou Zhang
Dr. Zhengxia Zou
Dr. ZhiYong Lv
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 papers will be 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 2000 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
  • Pattern recognition
  • Deep learning
  • Domain adaptation
  • Active learning
  • Manifold learning
  • Semantic segmentation
  • Data fusion

Published Papers (3 papers)

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Research

Open AccessArticle
Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification
Remote Sens. 2020, 12(2), 280; https://doi.org/10.3390/rs12020280 - 15 Jan 2020
Abstract
In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional [...] Read more.
In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images. Full article
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Open AccessArticle
Do Game Data Generalize Well for Remote Sensing Image Segmentation?
Remote Sens. 2020, 12(2), 275; https://doi.org/10.3390/rs12020275 - 14 Jan 2020
Abstract
Despite the recent progress in deep learning and remote sensing image interpretation, the adaption of a deep learning model between different sources of remote sensing data still remains a challenge. This paper investigates an interesting question: do synthetic data generalize well for remote [...] Read more.
Despite the recent progress in deep learning and remote sensing image interpretation, the adaption of a deep learning model between different sources of remote sensing data still remains a challenge. This paper investigates an interesting question: do synthetic data generalize well for remote sensing image applications? To answer this question, we take the building segmentation as an example by training a deep learning model on the city map of a well-known video game “Grand Theft Auto V” and then adapting the model to real-world remote sensing images. We propose a generative adversarial training based segmentation framework to improve the adaptability of the segmentation model. Our model consists of a CycleGAN model and a ResNet based segmentation network, where the former one is a well-known image-to-image translation framework which learns a mapping of the image from the game domain to the remote sensing domain; and the latter one learns to predict pixel-wise building masks based on the transformed data. All models in our method can be trained in an end-to-end fashion. The segmentation model can be trained without using any additional ground truth reference of the real-world images. Experimental results on a public building segmentation dataset suggest the effectiveness of our adaptation method. Our method shows superiority over other state-of-the-art semantic segmentation methods, for example, Deeplab-v3 and UNet. Another advantage of our method is that by introducing semantic information to the image-to-image translation framework, the image style conversion can be further improved. Full article
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Open AccessArticle
Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer
Remote Sens. 2020, 12(1), 174; https://doi.org/10.3390/rs12010174 - 03 Jan 2020
Abstract
Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the [...] Read more.
Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps. Full article
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