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Remote Sensing
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Change Detection and Classification with Hyperspectral Imaging
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Change Detection and Classification with Hyperspectral Imaging

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

Deadline for manuscript submissions: 30 July 2025 | Viewed by 1272

Special Issue Editors

Dr. Xinxin Liu

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: remote sensing image processing and applications
Dr. Bin Yang

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Interests: change detection; deep learning; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Qian Shi

E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
Interests: deep learning; machine learning; hyperspectral image; high resolution image
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lin Lei

E-Mail Website
Guest Editor
College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: deep learning; change detection; heterogenous images
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral imaging is a pivotal technology in the field of remote sensing, offering high-resolution spectral information for Earth observation. This technology excels in capturing subtle spectral differences, which is crucial in monitoring natural and human-induced changes. Despite significant advancements, several challenges remain in classifying and interpreting these spectral signatures with high accuracy and in capturing and detecting essential changes over time. The incorporation of machine learning and artificial intelligence promises to unlock new levels of accuracy and efficiency in classification and change detection.

This Special Issue will bring together the latest research and innovative methodologies in hyperspectral image classification and change detection. We will also explore the potential of hyperspectral technology in various applications, bridging the gap between theoretical research and practical applications, including, but not limited to, environmental monitoring, urban planning, and agricultural management.

Dr. Xinxin Liu
Dr. Bin Yang
Dr. Qian Shi
Dr. Lin Lei
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

  • advanced algorithms for hyperspectral image classification
  • machine learning and deep learning approaches for change detection
  • hybrid approaches combining traditional and AI-based methods
  • validation techniques for hyperspectral change detection
  • integration of hyperspectral data with other remote sensing technologies
  • applications of hyperspectral classification or change detection in real-world problem-solving
  • case studies of technological breakthroughs in classification or change detection
  • time-series analysis of hyperspectral data for trend identification

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Published Papers (1 paper)

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Research

22 pages, 4288 KiB  
Article
Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat
by Sandra Skendžić, Hrvoje Novak, Monika Zovko, Ivana Pajač Živković, Vinko Lešić, Marko Maričević and Darija Lemić
Remote Sens. 2025, 17(5), 929; https://doi.org/10.3390/rs17050929 - 5 Mar 2025
Viewed by 730
Abstract
Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. [...] Read more.
Aphids are significant pests of winter wheat, causing damage by feeding on plant sap and reducing crop yield and quality. This study evaluates the potential of hyperspectral remote sensing (350–2500 nm) and machine learning (ML) models for classifying healthy and aphid-infested wheat canopies. Field-based hyperspectral measurements were conducted at three growth stages—T1 (stem elongation–heading), T2 (flowering), and T3 (milky grain development)—with infestation levels categorized according to established economic thresholds (ET) for each growth stage. Spectral data were analyzed using Uniform Manifold Approximation and Projection (UMAP); vegetation indices; and ML classification models, including Logistic Regression (LR), k-Nearest Neighbors (KNNs), Support vector machines (SVMs), Random Forest (RF), and Light Gradient Boosting Machine (LGBM). The classification models achieved high performance, with F1-scores ranging from 0.88 to 0.99, and SVM and RF consistently outperforming other models across all input datasets. The best classification results were obtained at T2 with an F1-score of 0.98, while models trained on the full spectrum dataset showed the highest overall accuracy. Among vegetation indices, the Modified Triangular Vegetation Index, MTVI (rpb = −0.77 to −0.82), and Triangular Vegetation Index, TVI (rpb = −0.66 to −0.75), demonstrated the strongest correlations with canopy condition. These findings underscore the utility of canopy spectra and vegetation indices for detecting aphid infestations above ET levels, allowing for a clear classification of wheat fields into “treatment required” and “no treatment required” categories. This approach provides a precise and timely decision making tool for insecticide application, contributing to sustainable pest management by enabling targeted interventions, reducing unnecessary pesticide use, and supporting effective crop protection practices. Full article
(This article belongs to the Special Issue Change Detection and Classification with Hyperspectral Imaging)
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