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Remote Sensing
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Land Cover Change Detection and Mapping Based on Remote Sensing...
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Special Issue "Land Cover Change Detection and Mapping Based on Remote Sensing and Artificial Intelligence"

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

Deadline for manuscript submissions: 19 January 2024 | Viewed by 1882

Special Issue Editors

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 710054, China
Interests: change detection; land cover classification; remote sensing images
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weiwei Sun

E-Mail Website
Guest Editor
Department of Geography and Spatial Information Techniques, Ningbo University Beijing, Ningbo 315211, China
Interests: remote sensing; hyperspectral image processing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Yang
E-Mail Website
Guest Editor
Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315201, China
Interests: coastal remote sensing; remote sensing time-series products temporal reconstruction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jon Atli Benediktsson

grade E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, University of Iceland, IS 102 Reykjavík, Iceland
Interests: remote sensing; image analysis; pattern recognition; signal processing
Special Issues, Collections and Topics 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, USA
Interests: crop yield prediction and phenotyping
Special Issues, Collections and Topics in MDPI journals
Dr. Nicola Falco

E-Mail Website
Guest Editor
Lawrence Berkeley National Laboratory, Climate and Ecosystem Sciences Division, Building 085B, M/S 74R316C, Berkeley, CA, USA
Interests: signal and image processing; machine learning for remote sensing; multimodal data integration; hyperspectral data analysis; remote sensing for precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover mapping is an essential part of the earth’s ecosystem, which has an important influence on ecological environment monitoring, carbon cycle simulation, climate change, and so on. Fine and high-quality land use data is the basis for grasping the dynamic and scale of land cover, predicting the development trend, and optimizing the allocation of natural resources.

Current land use mapping is difficult to meet the needs of land delicacy management in terms of spatial scale, data accuracy, and mapping means. With the development of big data and remote sensing, land cover data can be obtained by using MODIS, Landsat, and other satellite data besides ground measurement. However, the accuracy and reliability of data acquired based on a single method and single source are not high. The artificial intelligence methods represented by machine learning and deep learning provide abundant data sources and new technical means for urban land use fine mapping.

The main goal of this Special Issue is to provide a scientific platform to discuss recent advances in the application of remote sensing and artificial intelligence techniques in land cover mapping. Papers of both theoretical and applicative nature, as well as contributions regarding new advanced artificial learning and data science techniques for the remote sensing research community, are welcome.

Dr. Zhiyong Lv
Prof. Dr. Weiwei Sun
Dr. Gang Yang
Prof. Dr. Jon Atli Benediktsson
Dr. Zhou Zhang
Dr. Nicola Falco
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

  • land cover mapping
  • change detection
  • land cover and land use analysis
  • artificial intelligence

Published Papers (2 papers)

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19 pages, 7547 KiB  
Article
Semi-Supervised Urban Change Detection Using Multi-Modal Sentinel-1 SAR and Sentinel-2 MSI Data
by
Sebastian Hafner
,
Yifang Ban
and
Andrea Nascetti
Remote Sens. 2023, 15(21), 5135; https://doi.org/10.3390/rs15215135 - 27 Oct 2023
Viewed by 606
Abstract
Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint [...] Read more.
Urbanization is progressing at an unprecedented rate in many places around the world. The Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) missions, combined with deep learning, offer new opportunities to accurately monitor urbanization at a global scale. Although the joint use of SAR and optical data has recently been investigated for urban change detection, existing data fusion methods rely heavily on the availability of sufficient training labels. Meanwhile, change detection methods addressing label scarcity are typically designed for single-sensor optical data. To overcome these limitations, we propose a semi-supervised urban change detection method that exploits unlabeled Sentinel-1 SAR and Sentinel-2 MSI data. Using bitemporal SAR and optical image pairs as inputs, the proposed multi-modal Siamese network predicts urban changes and performs built-up area segmentation for both timestamps. Additionally, we introduce a consistency loss, which penalizes inconsistent built-up area segmentation across sensor modalities on unlabeled data, leading to more robust features. To demonstrate the effectiveness of the proposed method, the SpaceNet 7 dataset, comprising multi-temporal building annotations from rapidly urbanizing areas across the globe, was enriched with Sentinel-1 SAR and Sentinel-2 MSI data. Subsequently, network performance was analyzed under label-scarce conditions by training the network on different fractions of the labeled training set. The proposed method achieved an F1 score of 0.555 when using all available training labels, and produced reasonable change detection results (F1 score of 0.491) even with as little as 10% of the labeled training data. In contrast, multi-modal supervised methods and semi-supervised methods using optical data failed to exceed an F1 score of 0.402 under this condition. Code and data are made publicly available. Full article
(This article belongs to the Special Issue Land Cover Change Detection and Mapping Based on Remote Sensing and Artificial Intelligence)
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18 pages, 5437 KiB  
Article
Improving the Transferability of Deep Learning Models for Crop Yield Prediction: A Partial Domain Adaptation Approach
by
Yuchi Ma
,
Zhengwei Yang
,
Qunying Huang
and
Zhou Zhang
Remote Sens. 2023, 15(18), 4562; https://doi.org/10.3390/rs15184562 - 16 Sep 2023
Viewed by 819
Abstract
Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop [...] Read more.
Over the past few years, there has been extensive exploration of machine learning (ML), especially deep learning (DL), for crop yield prediction, resulting in impressive levels of accuracy. However, such models are highly dependent on training samples with ground truth labels (i.e., crop yield records), which are not available in some regions. Additionally, due to the existence of domain shifts between different spatial regions, DL models trained within one region (i.e., source domain) tend to have poor performance when directly applied to other regions (i.e., target domain). Unsupervised domain adaptation (UDA) has become a promising strategy to improve the transferability of DL models by aligning the feature distributions in the source domain and the target domain. Despite the success, existing UDA models generally assume an identical label space across different domains. This assumption can be invalid in crop yield prediction scenarios, as crop yields can vary significantly in heterogeneous regions. Due to the mismatch between label spaces, negative transfer may occur if the entire source and target domains are forced to align. To address this issue, we proposed a novel partial domain adversarial neural network (PDANN), which relaxes the assumption of fully, equally shared label spaces across domains by downweighing the outlier source samples. Specifically, during model training, the PDANN weighs each labeled source sample based on the likelihood of its yield value given the expected target yield distribution. Instead of aligning the target domain to the entire source domain, the PDANN model downweighs the outlier source samples and performs partial weighted alignment of the target domain to the source domain. As a result, the negative transfer caused by source samples in the outlier label space would be alleviated. In this study, we assessed the model’s performance on predicting yields for two main commodities in the U.S., including corn and soybean, using the U.S. corn belt as the study region. The counties under study were divided into two distinct ecological zones and alternatively used as the source and target domains. Feature variables, including time-series vegetation indices (VIs) and sequential meteorological variables, were collected and aggregated at the county level. Next, the PDANN model was trained with the extracted features and corresponding crop yield records from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated for three testing years from 2019 to 2021. The experimental results showed that the developed PDANN model had achieved a mean coefficient of determination (R2) of 0.70 and 0.67, respectively, in predicting corn and soybean yields, outperforming three other ML and UDA models by a large margin from 6% to 46%. As the first study performing partial domain adaptation for crop yield prediction, this research demonstrates a novel solution for addressing negative transfer and improving DL models’ transferability on crop yield prediction. Full article
(This article belongs to the Special Issue Land Cover Change Detection and Mapping Based on Remote Sensing and Artificial Intelligence)
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