Special Issue "Remote Sensing for Land Cover and Vegetation Mapping"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Fernando Vicente-Guijalba
Website
Guest Editor
Dares Technology S.L., Spain
Interests: SAR; interferometry; remote sensing; land cover; mapping; machine learning

Special Issue Information

Dear Colleagues,

Land cover and vegetation mapping represents an invaluable product for land use and land management. From the local to global scale, land cover products help to plan and optimize the limited resources our planet provides. Remote sensing techniques have shown their capabilities in obtaining reliable and recurrent information regarding the nature and condition of surfaces. The broad diversity of technologies also allows us to sense different aspects of the surface, such as moist conditions, biochemical and structural elements, etc. Moreover, the dynamic nature of particular land surfaces driven by seasonal (e.g., crop rotation from winter to summer) or trend changes (e.g., land cover transition due to the climatic change) means that we must frequently observe the areas in order to track the cover changes with the aim of maintaining an updated cover map.

This Special Issue is focused on compiling the state-of-the-art research that specifically addresses aspects of the LC (land cover) and vegetation mapping from a remote sensing perspective, including but not limited to research on a regional to global scale, the role of passive and active sensors, capabilities and limitations in detecting similar type of covers, new technologies such as interferometric products, and state-of-the art algorithms for classification. Review contributions are welcomed as well as papers describing new measurement concepts and sensors.

Dr. Fernando Vicente-Guijalba
Guest Editor

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
  • land cover and land use
  • land cover dynamics
  • vegetation mapping and identification
  • machine learning
  • EO data classification
  • mapping
  • EO data fusion and assimilation

Published Papers (1 paper)

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Research

Open AccessArticle
Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks
Remote Sens. 2020, 12(10), 1676; https://doi.org/10.3390/rs12101676 - 23 May 2020
Abstract
Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on [...] Read more.
Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discriminative features. In the second phase, we used class attributes learned from the Word2Vec model (pre-trained by Google News) to train a second CNN model that performed class signature prediction by using both the features extracted by the first CNN and class attributes during training and only the features during prediction. We trained and tested our models on datasets collected over two subareas in the Cameron Highlands (training dataset, first test dataset) and Ipoh (second test dataset) in Malaysia. Several experiments have been conducted on the feature extraction and classification models regarding the main parameters, such as the network’s layers and depth, number of filters, and the impact of Gaussian noise. As a result, the best models were selected using various accuracy metrics such as top-k categorical accuracy for k = [1,2,3], Recall, Precision, and F1-score. The best model for feature extraction achieved 0.953 F1-score, 0.941 precision, 0.882 recall for the training dataset and 0.904 F1-score, 0.869 precision, 0.949 recall for the first test dataset, and 0.898 F1-score, 0.870 precision, 0.838 recall for the second test dataset. The best model for classification achieved an average of 0.778 top-one, 0.890 top-two and 0.942 top-three accuracy, 0.798 F1-score, 0.766 recall and 0.838 precision for the first test dataset and 0.737 top-one, 0.906 top-two, 0.924 top-three, 0.729 F1-score, 0.676 recall and 0.790 precision for the second test dataset. The results demonstrated that the proposed ZSL is a promising tool for land cover mapping based on high-resolution photos. Full article
(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)
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