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New Methods and Approaches in Airborne Hyperspectral Data Processing

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2064

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Special Issue Editors

Prof. Dr. Meiping Song

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Guest Editor

Center for Hyperspectral Imaging in Remote Sensing (CHIRS), Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
Interests: hyperspectral image processing; artificial intelligence; remote sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bing Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; dynamic monitoring of global resource environment with remote sensing; intelligent interpretation of remotely sensed big data
Special Issues, Collections and Topics in MDPI journals

Dr. Fadi Kizel

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Guest Editor

Department of Mapping and Geoinformation Engineering, Civil and Environmental Engineering, Technion-Israel Institute of Technology, Haifa, Israel
Interests: hyperspectral image and data processing; spectral unmixing; remote sensing; data fusion; hyperspectral data correction and calibration

Dr. Bai Xue

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Guest Editor

Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
Interests: hyperspectral imaging; remote sensing; pattern recognition; machine learning; image processing; signal processing; data science (spectroscopy)

Dr. Haitao Zhao

E-Mail Website
Guest Editor

Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: direct georeferencing; geometry rectification of hyperspectral image

Special Issue Information

Dear Colleagues,

Hyperspectral imaging technologies have been widely used in many remote sensing applications, resulting in large quantities of hyperspectral image datasets. Therefore, the efficient acquisition, storage, transmission, and analysis of these massive image datasets has become challenging, especially for many onboard applications with severely constrained computing resources and communication bandwidths. Particular challenges in the processing of hyperspectral data are how to deal with high-volume data with limited spatial or spectral (for multispectral systems) resolutions, especially for reducing data or enhancing the (spatial or spectral) resolution and fusion of the spatial and spectral information for improved data prediction (i.e., classification and regression analysis). Many algorithms and techniques have been proposed and continue to be needed to address such challenges.

The aim of this Special Issue is thus to focus on and compile the latest advances related to Airborne Hyperspectral Data Processing. All contributions to such hyperspectral sensing systems offering timely high-quality observational capabilities for better sensing that meet the end-users’ requirements and expectations for interdisciplinary applications are welcome.

This Special Issue is devoted to novel processing techniques for hyperspectral image data using hardware or software solutions. We solicit your contributions addressing hyperspectral data processing and applications based on the following methods:

Anomaly detection and target detection;
Hyperspectral image correction and calibration;
Applications of multispectral/hyperspectral imaging;
Band selection, dimensionality reduction and data compression;
Compressive sensing, sparse representation and tensor decomposition;
Unsupervised learning, active learning and deep learning;
Data/sensor/information fusion;
Endmember finding, extraction and variability;
High-performance computing;
Multispectral/hyperspectral image classification;
Hyperspectral unmixing;
Subpixel target analysis;
Hyperspectral data visualization.

Dr. Meiping Song
Prof. Dr. Bing Zhang
Dr. Fadi Kizel
Dr. Bai Xue
Dr. Haitao Zhao
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

hyperspectral unmixing
hyperspectral detection
hyperspectral representation
hyperspectral compression
hyperspectral real-time processing
hyperspectral fusion
hyperspectral applications
multispectral/hyperspectral image classification
advanced airborne hyperspectral sensors
geometry rectification and mosaic of hyperspectral image

Published Papers (2 papers)

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Research

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34 pages, 6927 KiB

Open AccessArticle

Efficient Blind Hyperspectral Unmixing Framework Based on CUR Decomposition (CUR-HU)

by Muhammad A. A. Abdelgawad, Ray C. C. Cheung and Hong Yan

Remote Sens. 2024, 16(5), 766; https://doi.org/10.3390/rs16050766 - 22 Feb 2024

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Abstract

Hyperspectral imaging captures detailed spectral data for remote sensing. However, due to the limited spatial resolution of hyperspectral sensors, each pixel of a hyperspectral image (HSI) may contain information from multiple materials. Although the hyperspectral unmixing (HU) process involves estimating endmembers, identifying pure spectral components, and estimating pixel abundances, existing algorithms mostly focus on just one or two tasks. Blind source separation (BSS) based on nonnegative matrix factorization (NMF) algorithms identify endmembers and their abundances at each pixel of HSI simultaneously. Although they perform well, the factorization results are unstable, require high computational costs, and are difficult to interpret from the original HSI. CUR matrix decomposition selects specific columns and rows from a dataset to represent it as a product of three small submatrices, resulting in interpretable low-rank factorization. In this paper, we propose a new blind HU framework based on CUR factorization called CUR-HU that performs the entire HU process by exploiting the low-rank structure of given HSIs. CUR-HU incorporates several techniques to perform the HU process with a performance comparable to state-of-the-art methods but with higher computational efficiency. We adopt a deterministic sampling method to select the most informative pixels and spectrum components in HSIs. We use an incremental QR decomposition method to reduce computation complexity and estimate the number of endmembers. Various experiments on synthetic and real HSIs are conducted to evaluate the performance of CUR-HU. CUR-HU performs comparably to state-of-the-art methods for estimating the number of endmembers and abundance maps, but it outperforms other methods for estimating the endmembers and the computational efficiency. It has a

9.4

249.5

times speedup over different methods for different real HSIs. Full article

(This article belongs to the Special Issue New Methods and Approaches in Airborne Hyperspectral Data Processing)

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14 pages, 5023 KiB

Open AccessTechnical Note

Stain Detection Based on Unmanned Aerial Vehicle Hyperspectral Photovoltaic Module

by Da Li, Lan Li, Mingyang Cui, Pengliang Shi, Yintong Shi, Jian Zhu, Sui Dai and Meiping Song

Remote Sens. 2024, 16(1), 153; https://doi.org/10.3390/rs16010153 - 29 Dec 2023

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Abstract

Solar power generation has great development potential as an abundant and clean energy source. However, many factors affect the efficiency of the photovoltaic (PV) module; among these factors, outdoor PV modules are inevitably affected by stains, thus reducing the power generation efficiency of the PV panel. This paper proposes a framework for PV module stain detection based on UAV hyperspectral images (HSIs). The framework consists of two stain detection methods: constrained energy minimization (CEM)-based and orthogonal subspace projection (OSP)-based stain detection methods. Firstly, the contaminated PV modules are analyzed and processed to enhance the data’s analytical capability. Secondly, based on the known spectral signature of the PV module, stain detection methods are proposed, including CEM-based stain detection and OSP-based stain detection for PV modules. The experimental results on real data illustrate that, in comparison with contrasting methods, the proposed method achieves stain detection results that closely align with known stain percentages. Additionally, it exhibits a fitting curve similar to the more maturely developed electroluminescence-based methods currently in use. Full article

(This article belongs to the Special Issue New Methods and Approaches in Airborne Hyperspectral Data Processing)

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