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Remote Sensing Technology for Agricultural and Land Management
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Remote Sensing Technology for Agricultural and Land Management

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: 25 November 2024 | Viewed by 536

Special Issue Editor

Prof. Dr. Paul Krause

E-Mail Website
Guest Editor
Computer Science Research Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: machine learning; good old fashioned AI; ecological modelling; medical AI; AI and environmental modelling/monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With climate change and biodiversity crises, we need to rethink the way we perform agriculture and land management. Working with nature rather than trying to control it has the potential to increase carbon capture and biodiversity in agricultural units without sacrificing productivity. With regard to the management of the wider landscape, practices in “rewilding” are showing their potential to enhance the ecosystem services such as flood control, water quality, pollination, and amenity that wild spaces provide.

We are still learning how to manage this change effectively. Continuous assessment of land holdings is needed in order to ensure that the management goals are being met. This can only be achieved cost-effectively at scale through the use of remote sensing. This Special Issue will focus on techniques and case studies that build an experience base for the use of remote sensing to manage agricultural units and natural spaces in an environmentally positive and sustainable way.

Submissions of research papers, case studies, and review articles are welcome.

Prof. Dr. Paul Krause
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 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. Sensors 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 2600 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

  • sensor networks
  • Internet of Things
  • environmental monitoring
  • satellite image analysis
  • ecological intensification
  • conservation agriculture
  • ecosystem services
  • biodiversity assessment

Published Papers (1 paper)

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Research

28 pages, 16525 KiB  
Article
DMAF-NET: Deep Multi-Scale Attention Fusion Network for Hyperspectral Image Classification with Limited Samples
by Hufeng Guo and Wenyi Liu
Sensors 2024, 24(10), 3153; https://0-doi-org.brum.beds.ac.uk/10.3390/s24103153 - 15 May 2024
Viewed by 412
Abstract
In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the [...] Read more.
In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several critical issues that need to be addressed in the HSIC task, such as the lack of labeled training samples, which constrains the classification accuracy and generalization ability of CNNs. To address this problem, a deep multi-scale attention fusion network (DMAF-NET) is proposed in this paper. This network is based on multi-scale features and fully exploits the deep features of samples from multiple levels and different perspectives with an aim to enhance HSIC results using limited samples. The innovation of this article is mainly reflected in three aspects: Firstly, a novel baseline network for multi-scale feature extraction is designed with a pyramid structure and densely connected 3D octave convolutional network enabling the extraction of deep-level information from features at different granularities. Secondly, a multi-scale spatial–spectral attention module and a pyramidal multi-scale channel attention module are designed, respectively. This allows modeling of the comprehensive dependencies of coordinates and directions, local and global, in four dimensions. Finally, a multi-attention fusion module is designed to effectively combine feature mappings extracted from multiple branches. Extensive experiments on four popular datasets demonstrate that the proposed method can achieve high classification accuracy even with fewer labeled samples. Full article
(This article belongs to the Special Issue Remote Sensing Technology for Agricultural and Land Management)
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