Hyperspectral Band Selection for Ground Fuel Classification for Prescribed Fires.
Saved in:
| Title: | Hyperspectral Band Selection for Ground Fuel Classification for Prescribed Fires. |
|---|---|
| Authors: | Karankot, Mahmad Isaq1 (AUTHOR) mahmadisaq.karankot@student.montana.edu, Glenn, Ethan M.1,2 (AUTHOR), Masood, Muhammad Umer1,2 (AUTHOR), Zhou, Xiaobing2 (AUTHOR), Whitaker, Bradley M.1 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1440. 37p. |
| Subjects: | Prescribed burning, Dimensional reduction algorithms, Machine learning, Ground vegetation cover, Hyperspectral imaging systems, Remote sensing, Deep learning |
| Abstract: | Highlights: What are the main findings? The comparative evaluation of five hyperspectral band-selection methods–PCA, SSEP, SRPA, and DRL and a clustering based baseline (K-Means Clustering-Based Band Selection: KMCBS)–for ground fuel classification in prescribed-fire environments. Selected spectral bands enable machine learning and deep learning models (RF, SVM, KNN, and 3D-CNN) to achieve competitive classification accuracy across benchmark datasets and a UAV-based VNIR hyperspectral dataset collected after prescribed burns. What are the implications of the main finding? Effective band selection reduces hyperspectral dimensionality and improves computational efficiency for large UAV hyperspectral datasets. The framework supports the scalable AI-driven monitoring of ground fuels and post-fire vegetation conditions using hyperspectral remote sensing. Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms' evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? The comparative evaluation of five hyperspectral band-selection methods–PCA, SSEP, SRPA, and DRL and a clustering based baseline (K-Means Clustering-Based Band Selection: KMCBS)–for ground fuel classification in prescribed-fire environments. Selected spectral bands enable machine learning and deep learning models (RF, SVM, KNN, and 3D-CNN) to achieve competitive classification accuracy across benchmark datasets and a UAV-based VNIR hyperspectral dataset collected after prescribed burns. What are the implications of the main finding? Effective band selection reduces hyperspectral dimensionality and improves computational efficiency for large UAV hyperspectral datasets. The framework supports the scalable AI-driven monitoring of ground fuels and post-fire vegetation conditions using hyperspectral remote sensing. Hyperspectral image (HSI) analysis plays a central role in remote sensing tasks requiring fine-grained material discrimination, vegetation health assessment, and post-disturbance monitoring. Yet, the high dimensionality and strong spectral redundancy in HSIs often reduce the efficiency and reliability of machine learning models. These challenges are especially important in wildfire science and prescribed-fire monitoring, where spectral responses vary due to burn severity, char deposition, canopy structure, and early vegetation recovery. Benchmark datasets such as Indian Pines and Pavia University and others provide controlled environments for algorithms' evaluation, but real-world post-fire forest conditions pose additional complexity. This study presents a unified and comprehensive evaluation of five dimensionality reduction strategies: Principal Component Analysis (PCA), Spatial–Spectral Edge Preservation (SSEP), Spectral-Redundancy Penalized Attention (SRPA), and a Deep Reinforcement Learning (DRL)-based selector together with a clustering based baseline, K-Means Clustering-Based Band Selection (KMCBS). These strategies are combined with classical machine learning and deep learning classifiers: Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNNs), and 3D Convolutional Neural Networks (3D-CNN). The full pipeline includes exploratory data analysis, preprocessing, patch-based spatial–spectral modeling, consistent train–validation protocols, and multi-dataset evaluation across Indian Pines, Pavia University, and a new custom VNIR hyperspectral dataset collected after prescribed burns at the Lubrecht Experimental Forest in Montana, USA. By systematically comparing statistical, edge-aware, attention-guided, and reinforcement learning-based band-selection strategies, this work identifies compact yet informative spectral subsets that enhance classification performance while reducing computational cost. Importantly, the inclusion of the Montana prescribed-burn dataset provides a unique real-world testbed for understanding band selection behavior in fire-affected forest environments. Overall, this study contributes a generalizable and extensible framework for HSI dimensionality reduction and classification, laying the groundwork for future applications in wildfire assessment, vegetation recovery monitoring, and remote sensing. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091440 |
