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

Deadline for manuscript submissions: closed (1 November 2025) | Viewed by 4613

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Prof. Dr. A. G. Unil Perera

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Department of Physics & Astronomy, Georgia State University, Atlanta, GA 30303, USA
Interests: infrared sensing; nano and microstruture IR devices; medical diagnostics using infrared; plasmonic enhancements; novel materials
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Dear Colleagues,

Hyperspectral imaging has emerged as a groundbreaking technology, capturing detailed spectral information across a wide range of wavelengths to enable precise material identification and analysis. Its ability to collect rich spectral data without physical contact makes it a powerful tool across various disciplines, including environmental monitoring, agriculture and food safety, chemical and material characterization, medical and biomedical applications, defense and security, forensic science, and pharmaceutical quality control.

For example, in medicine, hyperspectral imaging is transforming non-invasive diagnostics by enabling early disease detection, tissue analysis, and surgical guidance. In chemical and material sciences, it provides unparalleled insights into molecular composition and structural properties.

This Special Issue, entitle “Hyperspectral Sensing: Imaging and Applications”, highlights the latest advancements in hyperspectral imaging techniques, data processing algorithms, and their multidisciplinary applications. We invite researchers to contribute original studies, reviews, and case studies that explore innovative methodologies, address challenges in spectral data analysis, and demonstrate the transformative potential of this technology in fields ranging from healthcare to environmental sustainability and material science.

Prof. Dr. A. G. Unil Perera
Guest Editor

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Keywords

hyperspectral imaging
remote sensing applications
spectral data analysis
environmental monitoring
medical diagnostics and applications
defense and security
agriculture and food safety
forensic and pharmaceutical quality control

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Published Papers (4 papers)

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Research

40 pages, 5095 KB

Open AccessArticle

When Lie Groups Meet Hyperspectral Images: Equivariant Manifold Network for Few-Shot HSI Classification

by Haolong Ban, Junchao Feng, Zejin Liu, Yue Jiang, Zhenxing Wang, Jialiang Liu, Yaowen Hu and Yuanshan Lin

Sensors 2026, 26(7), 2117; https://doi.org/10.3390/s26072117 - 29 Mar 2026

Viewed by 421

Abstract

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article

(This article belongs to the Special Issue Hyperspectral Sensing: Imaging and Applications)

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27 pages, 17269 KB

Open AccessArticle

Deep Architectures Fail to Generalize: A Lightweight Alternative for Agricultural Domain Transfer in Hyperspectral Images

by Praveen Pankajakshan, Aravind Padmasanan and S. Sundar

Sensors 2026, 26(1), 174; https://doi.org/10.3390/s26010174 - 26 Dec 2025

Viewed by 596

Abstract

We present a novel framework for hyperspectral satellite image classification that explicitly balances spatial nearness with spectral similarity. The proposed method is trained on closed-set datasets, and it generalizes well to open-set agricultural scenarios that include both class distribution shifts and presence of novel and absence of known classes. This scenario is reflective of real-world agricultural conditions, where geographic regions, crop types, and seasonal dynamics vary widely and labeled data are scarce and expensive. The input data are projected onto a lower-dimensional spectral manifold, and a pixel-wise classifier generates an initial class probability saliency map. A kernel-based spectral-spatial weighting strategy fuses the spatial-spectral features. The proposed approach improves the classification accuracy by

7.22

–

15 %

over spectral-only models on benchmark datasets. Incorporating an additional unsupervised learning refinement step further improves accuracy, surpassing several recent state-of-the-art methods. Requiring only 1–

10 %

labeled training data and at most two tuneable parameters, the framework operates with minimal computational overhead, qualifying it as a data-efficient and scalable few-shot learning solution. Recent deep architectures although exhibit high accuracy under data rich conditions, often show limited transferability under low-label, open-set agricultural conditions. We demonstrate transferability to new domains—including unseen crop classes (e.g., paddy), seasons, and regions (e.g., Piedmont, Italy)—without re-training. Rice paddy fields play a pivotal role in global food security but are also a significant contributor to greenhouse gas emissions, especially methane, and extent mapping is very critical. This work presents a novel perspective on hyperspectral classification and open-set adaptation, suited for sustainable agriculture with limited labels and low-resource domain generalization. Full article

(This article belongs to the Special Issue Hyperspectral Sensing: Imaging and Applications)

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29 pages, 5594 KB

Open AccessArticle

Assessing Changes in Grassland Species Distribution at the Landscape Scale Using Hyperspectral Remote Sensing

by Obumneke Ohiaeri, Carlos Portillo-Quintero and Haydee Laza

Sensors 2025, 25(22), 6821; https://doi.org/10.3390/s25226821 - 7 Nov 2025

Viewed by 1254

Abstract

The advancement of hyperspectral remote sensing technology has enhanced the ability to assess and characterize land cover in complex ecosystems. In this study, a linear spectral unmixing algorithm was applied to NEON hyperspectral imagery in 2018 and 2022 to quantify the fractional abundance of dominant land cover classes, namely herbaceous vegetation, mixed forbs, and bare soil, across the Marvin Klemme Experimental Rangeland in Oklahoma. UAV imagery acquired during the 2023 field campaign provided high resolution reference data for model training. The LSU results revealed a decline in herbaceous cover from 16.02 ha to 11.56 ha and an expansion of bare soil from 3.37 ha to 6.39 ha, while mixed forb cover remained relatively stable (12.38 ha to 13.82 ha). Accuracy assessment using the UAV-derived validation points yielded overall accuracy of 84% and 60% at fractional thresholds of 50% and 75%, respectively. Although statistical tests indicated no significant change in mean fractional abundance (p > 0.05), slope-based trend maps captured localized vegetation loss and regrowth patterns. These findings demonstrate the effectiveness of integrating LSU with UAV data for detecting subtle yet ecologically meaningful shifts in semi-arid grassland composition. Full article

(This article belongs to the Special Issue Hyperspectral Sensing: Imaging and Applications)

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18 pages, 18889 KB

Open AccessArticle

A Handheld Multispectral Device for Assessing Leaf Nitrogen Concentrations in Maize

by Felipe Hermínio Meireles Nogueira, Adunias dos Santos Teixeira, Sharon Gomes Ribeiro, Luís Clênio Jario Moreira, Odílio Coimbra da Rocha Neto, Fernando Bezerra Lopes and Ricardo Emílio Ferreira Quevedo Nogueira

Sensors 2025, 25(13), 3929; https://doi.org/10.3390/s25133929 - 24 Jun 2025

Cited by 4 | Viewed by 1680

Abstract

This study presents the MSPAT (Multispectral Soil Plant Analysis Tool), a device designed for assessing leaf nitrogen concentrations in maize crops under field conditions. The MSPAT includes the AS7265x sensor, which has 18 bands and covers the spectrum from 410 to 940 nm. This device was designed to be portable, using the ESP32 microcontroller and incorporating such functionalities as data storage on a MicroSD card, communication with a smartphone via Wi-Fi, and geolocation of acquired data. The MSPAT was evaluated in an experiment conducted at the Federal University of Ceará (UFC), where maize was subjected to different doses of nitrogen fertiliser (0, 60, 90, 120, 150, and 180 kg·ha⁻¹ N). Spectral readings were taken at three phenological stages (V5, V10, and R2) using the MSPAT, an SPAD-502 chlorophyll meter, and a FieldSpec PRO FR3 spectroradiometer. After the optical measurements were taken, the nitrogen concentrations in the leaves were determined in a laboratory by using the Kjeldahl method. The data analysis included the calculation of normalised ratio indices (NRIs) using linear regression and the application of multivariate statistical methods (PLSR and PCR) for predicting leaf nitrogen concentrations (LNCs). The best performance for the MSPAT index (NRI) was obtained using the 900 nm and the 560 nm bands (R² = 0.64) at stage V10. In the validation analysis, the MSPAT presented an R² of 0.79, showing performance superior to that of SPAD-502, which achieved an R² of 0.70. This confirms the greater potential of the MSPAT compared to commercial equipment and makes it possible to obtain results similar to those obtained using the reference spectroradiometer. The PLSR model with data from the FieldSpec 3 provided important validation metrics when using reflectance data with first-derivative transformation (R² = 0.88, RMSE = 1.94 and MAE = 1.28). When using the MSPAT, PLSR (R² = 0.75, RMSE = 2.77 and MAE = 2.26) exhibited values of metrics similar to those for PCR (R² = 0.75, RMSE = 2.78 and MAE = 2.26). This study validates the use of MSPAT as an effective tool for monitoring the nutritional status of maize to optimize the use of nitrogen fertilisers. Full article

(This article belongs to the Special Issue Hyperspectral Sensing: Imaging and Applications)

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