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Hyperspectral Remote Sensing: Current Situation and New Challenges

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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 (30 June 2022) | Viewed by 30639

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Dr. Bogdan Zagajewski

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Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, Warsaw University, Poland, ul. Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
Interests: imaging spectroscopy; classification; algorithms; vegetation; natural and semi-natural ecosystems; high-mountain and Arctic monitoring; land cover mapping
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Special Issue Information

Dear Colleagues,

The Special Issue is oriented on the hyperspectral remote sensing, we would like to collect a wide spectrum of the newest ideas, solutions and achievements in the fields of new missions & sensors, new methods of data acquisition, processing, verification and analysis. We are opened for manuscripts oriented on hyperspectral remote sensing application in multitemporal data analyses, proximal & field investigations, natural heritage, coastal zones, water, education & training, forestry, geological applications, land ice & snow, agriculture & soil, land use & land cover and urban remote sensing.

The aim of this special issue is to collect articles that will contribute new ideas and methods to process hyperspectral imaging data and the development of other applications. We invite to submit articles for the advancement of hyperspectral technology but not limited to the following topics:

Determining and mapping of vegetation traits using hyperspectral imaging;
Hyperspectral image applications in geology, agriculture, environment and urban systems;
Image processing and data mining (supervised, unsupervised, active learning and deep learning) methods for analysing hyperspectral images;
Hyperspectral unmixing;
Methods to compensate atmosphere in hyperspectral image processing;
Applications of future hyperspectral missions.

Dr. Bogdan Zagajewski
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. 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

imaging spectroscopy
hyperspectral imaging
remote sensing
data mining
image processing
atmosphere correction
cloud computing
sensors

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Published Papers (9 papers)

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Research

19 pages, 3428 KiB

Open AccessArticle

ES²FL: Ensemble Self-Supervised Feature Learning for Small Sample Classification of Hyperspectral Images

by Bing Liu, Kuiliang Gao, Anzhu Yu, Lei Ding, Chunping Qiu and Jia Li

Remote Sens. 2022, 14(17), 4236; https://doi.org/10.3390/rs14174236 - 27 Aug 2022

Cited by 14 | Viewed by 2169

Abstract

Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES

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FL) method is proposed in [...] Read more.

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FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial–spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES

^{2}

FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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22 pages, 4474 KiB

Open AccessArticle

Standoff Infrared Measurements of Chemical Plume Dynamics in Complex Terrain Using a Combination of Active Swept-ECQCL Laser Spectroscopy with Passive Hyperspectral Imaging

by Mark C. Phillips, Bruce E. Bernacki, Patrick T. Conry and Michael J. Brown

Remote Sens. 2022, 14(15), 3756; https://doi.org/10.3390/rs14153756 - 5 Aug 2022

Cited by 2 | Viewed by 2149

Abstract

Chemical plume detection and modeling in complex terrain present numerous challenges. We present experimental results from outdoor releases of two chemical tracers (sulfur hexafluoride and Freon-152a) from different locations in mountainous terrain. Chemical plumes were detected using two standoff instruments collocated at a distance of 1.5 km from the plume releases. A passive long-wave infrared hyperspectral imaging system was used to show time- and space-resolved plume transport in regions near the source. An active infrared swept-wavelength external cavity quantum cascade laser system was used in a standoff configuration to measure quantitative chemical column densities with high time resolution and high sensitivity along a single measurement path. Both instruments provided chemical-specific detection of the plumes and provided complementary information over different temporal and spatial scales. The results show highly variable plume propagation dynamics near the release points, strongly dependent on the local topography and winds. Effects of plume stagnation, plume splitting, and plume mixing were all observed and are explained based on local topographic and wind conditions. Measured plume column densities at distances ~100 m from the release point show temporal fluctuations over ~1 s time scales and spatial variations over ~1 m length scales. The results highlight the need for high-speed and spatially resolved measurement techniques to provide validation data at the relevant spatial and temporal scales required for high-fidelity terrain-aware microscale plume propagation models. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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20 pages, 6891 KiB

Open AccessArticle

Multisensor UAS mapping of Plant Species and Plant Functional Types in Midwestern Grasslands

by Emma C. Hall and Mark J. Lara

Remote Sens. 2022, 14(14), 3453; https://doi.org/10.3390/rs14143453 - 18 Jul 2022

Cited by 6 | Viewed by 2526

Abstract

Uncrewed aerial systems (UASs) have emerged as powerful ecological observation platforms capable of filling critical spatial and spectral observation gaps in plant physiological and phenological traits that have been difficult to measure from space-borne sensors. Despite recent technological advances, the high cost of drone-borne sensors limits the widespread application of UAS technology across scientific disciplines. Here, we evaluate the tradeoffs between off-the-shelf and sophisticated drone-borne sensors for mapping plant species and plant functional types (PFTs) within a diverse grassland. Specifically, we compared species and PFT mapping accuracies derived from hyperspectral, multispectral, and RGB imagery fused with light detection and ranging (LiDAR) or structure-for-motion (SfM)-derived canopy height models (CHM). Sensor–data fusion were used to consider either a single observation period or near-monthly observation frequencies for integration of phenological information (i.e., phenometrics). Results indicate that overall classification accuracies for plant species and PFTs were highest in hyperspectral and LiDAR-CHM fusions (78 and 89%, respectively), followed by multispectral and phenometric–SfM–CHM fusions (52 and 60%, respectively) and RGB and SfM–CHM fusions (45 and 47%, respectively). Our findings demonstrate clear tradeoffs in mapping accuracies from economical versus exorbitant sensor networks but highlight that off-the-shelf multispectral sensors may achieve accuracies comparable to those of sophisticated UAS sensors by integrating phenometrics into machine learning image classifiers. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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26 pages, 3771 KiB

Open AccessArticle

Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

by Wenmei Li, Huaihuai Chen, Qing Liu, Haiyan Liu, Yu Wang and Guan Gui

Remote Sens. 2022, 14(9), 2215; https://doi.org/10.3390/rs14092215 - 5 May 2022

Cited by 39 | Viewed by 5485

Abstract

Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural Network (CNN) has become one of the hot topics in the field of remote sensing. However, the high dimensional information and limited training samples are prone to the Hughes phenomenon for hyperspectral remote sensing images. Meanwhile, high-dimensional information processing also consumes significant time and computing power, or the extracted features may not be representative, resulting in unsatisfactory classification efficiency and accuracy. To solve these problems, an attention mechanism and depthwise separable convolution are introduced to the three-dimensional convolutional neural network (3DCNN). Thus, 3DCNN-AM and 3DCNN-AM-DSC are proposed for HRSI classification. Firstly, three hyperspectral datasets (Indian pines, University of Pavia and University of Houston) are used to analyze the patchsize and dataset allocation ratio (Training set: Validation set: Test Set) in the performance of 3DCNN and 3DCNN-AM. Secondly, in order to improve work efficiency, principal component analysis (PCA) and autoencoder (AE) dimension reduction methods are applied to reduce data dimensionality, and maximize the classification accuracy of the 3DCNN, but it will still take time. Furthermore, the HRSI classification model 3DCNN-AM and 3DCNN-AM-DSC are applied to classify with the three classic HRSI datasets. Lastly, the classification accuracy index and time consumption are evaluated. The results indicate that 3DCNN-AM could improve classification accuracy and reduce computing time with the dimension reduction dataset, and the 3DCNN-AM-DSC model can reduce the training time by a maximum of 91.77% without greatly reducing the classification accuracy. The results of the three classic hyperspectral datasets illustrate that 3DCNN-AM-DSC can improve the classification performance and reduce the time required for model training. It may be a new way to tackle hyperspectral datasets in HRSl classification tasks without dimensionality reduction. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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22 pages, 1144 KiB

Open AccessArticle

A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data

by Victor Andres Ayma Quirita, Gilson Alexandre Ostwald Pedro da Costa and César Beltrán

Remote Sens. 2022, 14(9), 2153; https://doi.org/10.3390/rs14092153 - 30 Apr 2022

Cited by 9 | Viewed by 2342

Abstract

In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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19 pages, 7437 KiB

Open AccessArticle

Using Ensemble-Based Systems with Near-Infrared Hyperspectral Data to Estimate Seasonal Snowpack Density

by Mohamed Karim El Oufir, Karem Chokmani, Anas El Alem and Monique Bernier

Remote Sens. 2022, 14(5), 1089; https://doi.org/10.3390/rs14051089 - 23 Feb 2022

Viewed by 2514

Abstract

Estimating the seasonal density of the snowpack has many financial and environmental benefits. Rapid assessment and daily monitoring of its evolution are therefore key to effective prevention. Traditionally, the physical characteristics of snow are measured directly in the field, which involves high costs and personnel mobilization. Hyperspectral imaging is a reliable and efficient technique to study and evaluate this physical property. The spectral reflectance of snow is partly defined by changes in its physical properties, particularly in the Near infrared (NIR) part of the spectrum. Recently, a hybrid snow density estimation model allowing retrieval of density from NIR hyperspectral data was developed, based on an a priori classification of snow samples. However, in order to obtain optimal density estimates with the Hybrid model (HM), the sources of classification and estimation error must be controlled. Following the same principle as the HM, an Ensemble-based system (EBS) was developed. This model reduces the number of misclassification errors produced by the HM. The general concept of EBS algorithms is based on the principle that obtaining more opinions before making a decision is part of human nature, especially when economic and environmental benefits are at stake. This approach has helped to reduce the risk of classification and estimation errors and to develop more robust density results. One hundred and fourteen snow samples collected during three winters (2018–2020) were used to calibrate and validate the EBS. The performance of the EBS was validated using an independent database and the results were satisfactory (R² = 0.90, RMSE = 44.45 kg m⁻³, BIAS = 3.87 kg m⁻³ and NASH = 0.89). Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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19 pages, 7174 KiB

Open AccessArticle

Mapping Invasive Plant Species with Hyperspectral Data Based on Iterative Accuracy Assessment Techniques

by Anita Sabat-Tomala, Edwin Raczko and Bogdan Zagajewski

Remote Sens. 2022, 14(1), 64; https://doi.org/10.3390/rs14010064 - 23 Dec 2021

Cited by 14 | Viewed by 4856

Abstract

Recent developments in computer hardware made it possible to assess the viability of permutation-based approaches in image classification. Such approaches sample a reference dataset multiple times in order to train an arbitrary number of machine learning models while assessing their accuracy. So-called iterative accuracy assessment techniques or Monte-Carlo-based approaches can be a useful tool when it comes to assessment of algorithm/model performance but are lacking when it comes to actual image classification and map creation. Due to the multitude of models trained, one has to somehow reason which one of them, if any, should be used in the creation of a map. This poses an interesting challenge since there is a clear disconnect between algorithm assessment and the act of map creation. Our work shows one of the ways this disconnect can be bridged. We calculate how often a given pixel was classified as given class in all variations of a multitude of post-classification images delivered by models trained during the iterative assessment procedure. As a classification problem, a mapping of Calamagrostis epigejos, Rubus spp., Solidago spp. invasive plant species using three HySpex hyperspectral datasets collected in June, August and September was used. As a classification algorithm, the support vector machine approach was chosen, with training hyperparameters obtained using a grid search approach. The resulting maps obtained F1-scores ranging from 0.87 to 0.89 for Calamagrostis epigejos, 0.89 to 0.97 for Rubus spp. and 0.99 for Solidago spp. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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11 pages, 2797 KiB

Open AccessCommunication

Regularized CNN Feature Hierarchy for Hyperspectral Image Classification

by Muhammad Ahmad, Manuel Mazzara and Salvatore Distefano

Remote Sens. 2021, 13(12), 2275; https://doi.org/10.3390/rs13122275 - 10 Jun 2021

Cited by 21 | Viewed by 2821

Abstract

Convolutional Neural Networks (CNN) have been rigorously studied for Hyperspectral Image Classification (HSIC) and are known to be effective in exploiting joint spatial-spectral information with the expense of lower generalization performance and learning speed due to the hard labels and non-uniform distribution over labels. Therefore, this paper proposed an idea to enhance the generalization performance of CNN for HSIC using soft labels that are a weighted average of the hard labels and uniform distribution over ground labels. The proposed method helps to prevent CNN from becoming over-confident. We empirically show that, in improving generalization performance, regularization also improves model calibration, which significantly improves beam-search. Several publicly available Hyperspectral datasets are used to validate the experimental evaluation, which reveals improved performance as compared to the state-of-the-art models with overall 99.29%, 99.97%, and 100.0% accuracy for Indiana Pines, Pavia University, and Salinas dataset, respectively. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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22 pages, 11289 KiB

Open AccessArticle

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

by Anna Jarocińska, Dominik Kopeć, Barbara Tokarska-Guzik and Edwin Raczko

Remote Sens. 2021, 13(1), 107; https://doi.org/10.3390/rs13010107 - 31 Dec 2020

Cited by 4 | Viewed by 3300

Abstract

The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)

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