Special Issue "Innovative Application of AI in Remote Sensing"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Wai Chi Fang
E-Mail Website
Guest Editor
National Chiao Tung University, Taiwan
Interests: VLSI bio-medical microsystems; neural networks and intelligent systems; multimedia signal processing; wireless communication; sensor networks; space integrated avionic systems
Special Issues and Collections in MDPI journals
Prof. Mincong Tang
E-Mail
Guest Editor
Beijing Jiaotong University, China
Interests: network security; computer network security
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Satellites have been flying around the Earth for decades now to scan landscapes and capture images in this ever-changing planet. Remote sensing is not a new element of the art and science of observing things from afar, but recent innovations with the application of artificial intelligence are very powerful and can potentially contribute a lot in society. At present, we are at a pivotal stage of a radical transformation regarding where information comes from and how it is analyzed and monetized. The application of artificial intelligence to remote sensing is the best and highest-quality technology in the classification of satellite global imagery that can help in overcoming the planets’ greatest challenges. Advantages in using clear and reliable satellite imagery include providing a wide image of the entire planet that can be used in helping society to predict climate change, prevent wars, stop forest fires, as well as solving the biggest questions and finding solutions in high resolution. Reducing costs is possible by replacing or optimizing the existing monitoring systems with the application of artificial intelligence in remote sensing.

This Special Issue aims to help to unlock the ability of satellite data using artificial intelligence in remote sensing. It will provide ideas about developing models that extract features, detecting changes and predicting physical situations using artificial intelligence.

Dr. Wai Chi Fang
Prof. Dr. Sabah Mohammed
Prof. Mincong Tang
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 2400 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

  • Application of AI in remote sensing
  • Challenges using artificial intelligence
  • Future strategies in remote Sensing
  • Advantages of AI in remote sensing
  • Significant impact of remote sensing
  • Satellite data and artificial intelligence
  • Powerful AI in remote sensing
  • How AI affects remote sensing
  • Demand of modern satellites
  • The changing planet satellites
  • Trends in remote sensing
  • Using AI in remote sensing for safety purposes
  • Latest remote sensing applications
  • Importance of AI in remote sensing
  • Effectiveness of remote sensing

Published Papers (2 papers)

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Article
Deep Learning Network Intensification for Preventing Noisy-Labeled Samples for Remote Sensing Classification
Remote Sens. 2021, 13(9), 1689; https://doi.org/10.3390/rs13091689 - 27 Apr 2021
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Abstract
The deep-learning-network performance depends on the accuracy of the training samples. The training samples are commonly labeled by human visual investigation or inherited from historical land-cover or land-use maps, which usually contain label noise, depending on subjective knowledge and the time of the [...] Read more.
The deep-learning-network performance depends on the accuracy of the training samples. The training samples are commonly labeled by human visual investigation or inherited from historical land-cover or land-use maps, which usually contain label noise, depending on subjective knowledge and the time of the historical map. Helping the network to distinguish noisy labels during the training process is a prerequisite for applying the model for training across time and locations. This study proposes an antinoise framework, the Weight Loss Network (WLN), to achieve this goal. The WLN contains three main parts: (1) the segmentation subnetwork, which any state-of-the-art segmentation network can replace; (2) the attention subnetwork (λ); and (3) the class-balance coefficient (α). Four types of label noise (an insufficient label, redundant label, missing label and incorrect label) were simulated by dilate and erode processing to test the network’s antinoise ability. The segmentation task was set to extract buildings from the Inria Aerial Image Labeling Dataset, which includes Austin, Chicago, Kitsap County, Western Tyrol and Vienna. The network’s performance was evaluated by comparing it with the original U-Net model by adding noisy training samples with different noise rates and noise levels. The result shows that the proposed antinoise framework (WLN) can maintain high accuracy, while the accuracy of the U-Net model dropped. Specifically, after adding 50% of dilated-label samples at noise level 3, the U-Net model’s accuracy dropped by 12.7% for OA, 20.7% for the Mean Intersection over Union (MIOU) and 13.8% for Kappa scores. By contrast, the accuracy of the WLN dropped by 0.2% for OA, 0.3% for the MIOU and 0.8% for Kappa scores. For eroded-label samples at the same level, the accuracy of the U-Net model dropped by 8.4% for OA, 24.2% for the MIOU and 43.3% for Kappa scores, while the accuracy of the WLN dropped by 4.5% for OA, 4.7% for the MIOU and 0.5% for Kappa scores. This result shows that the antinoise framework proposed in this paper can help current segmentation models to avoid the impact of noisy training labels and has the potential to be trained by a larger remote sensing image set regardless of the inner label error. Full article
(This article belongs to the Special Issue Innovative Application of AI in Remote Sensing)
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Technical Note
Fast and Accurate Terrain Image Classification for ASTER Remote Sensing by Data Stream Mining and Evolutionary-EAC Instance-Learning-Based Algorithm
Remote Sens. 2021, 13(6), 1123; https://doi.org/10.3390/rs13061123 - 16 Mar 2021
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Abstract
Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the [...] Read more.
Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%. Full article
(This article belongs to the Special Issue Innovative Application of AI in Remote Sensing)
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