Special Issue "Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing"

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 May 2021).

Special Issue Editors

Dr. Xavier Briottet
E-Mail Website
Guest Editor
Optics and Associated Techniques Department, ONERA, 2 Avenue Edouard Belin, 31005 Toulouse, France
Interests: high-spatial-resolution sensor; remote sensing signal and image processing; urban environment
Special Issues and Collections in MDPI journals
Dr. Thomas Corpetti
E-Mail Website
Guest Editor
CNRS – LETG Rennes, Place du Recheur Henri Le Moal, 35043 Rennes, CEDEX, France
Interests: remote sensing; urban environments; physical models; time series; data assimilation; classification; regression, fusion; pasture systems; agriculture
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

With the advent of new hyperspectral sensors aboard airborne or spaceborne platforms, remote sensing imaging spectroscopy can be applied to research in many fields, such as agriculture, biodiversity, mining, coastal zones, urban planning, defense. In fact, its ability to capture spectral feature characterizing the physical and chemical properties of scene materials opens the way to a better understanding and monitoring of a large variety of geographical areas.

Breakthroughs in the domain of machine learning over the past 10 years have motivated the remote sensing community to research in this direction, with results that outperform traditional approaches. In the context of hyperspectral data, thanks to its outstanding predictive capabilities, machine learning has become essential to automatically decipher the relationships between an optical/radiative property (reflectance, emissivity, radiance) and the corresponding information. Machine learning can already fulfil several tasks like target or anomaly detection, land cover classification, spectral unmixing, and physical/chemical parameter estimation. Nevertheless, several challenges to improve the performance of imaging spectroscopy with machine learning remain, such as the intrinsic dimensionality of hyperspectral images, the robustness and reliability of neural networks, spatio–temporal approaches, combinations with other measurements, imperfect and potentially large learning databases, lack of standardized datasets and experiments for benchmarking, complementarity between hyperspectral imagery and multimodal acquisitions, benefits of combining multitemporal hyperspectral images.

This Special Issue aims to present new and/or innovative methods, approaches, and products demonstrating the benefits of machine learning applied to hyperspectral imagery. Submissions will highlight how the scientific community tends to answer to these challenges.

Dr. Xavier Briottet
Dr. Thomas Corpetti
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 papers will be 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 2400 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
  • Multi-modality
  • Machine learning
  • Multi-temporal
  • Classification, regression, semantic segmentation
  • Unmixing
  • Deep learning

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information
Remote Sens. 2021, 13(12), 2382; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122382 - 18 Jun 2021
Viewed by 219
Abstract
Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI [...] Read more.
Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Figure 1

Article
Hyperspectral Unmixing Based on Constrained Bilinear or Linear-Quadratic Matrix Factorization
Remote Sens. 2021, 13(11), 2132; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112132 - 28 May 2021
Viewed by 493
Abstract
Unsupervised hyperspectral unmixing methods aim to extract endmember spectra and infer the proportion of each of these spectra in each observed pixel when considering linear mixtures. However, the interaction between sunlight and the Earth’s surface is often very complex, so that observed spectra [...] Read more.
Unsupervised hyperspectral unmixing methods aim to extract endmember spectra and infer the proportion of each of these spectra in each observed pixel when considering linear mixtures. However, the interaction between sunlight and the Earth’s surface is often very complex, so that observed spectra are then composed of nonlinear mixing terms. This nonlinearity is generally bilinear or linear quadratic. In this work, unsupervised hyperspectral unmixing methods, designed for the bilinear and linear-quadratic mixing models, are proposed. These methods are based on bilinear or linear-quadratic matrix factorization with non-negativity constraints. Two types of algorithms are considered. The first ones only use the projection of the gradient, and are therefore linked to an optimal manual choice of their learning rates, which remains the limitation of these algorithms. The second developed algorithms, which overcome the above drawback, employ multiplicative projective update rules with automatically chosen learning rates. In addition, the endmember proportions estimation, with three alternative approaches, constitutes another contribution of this work. Besides, the reduction of the number of manipulated variables in the optimization processes is also an originality of the proposed methods. Experiments based on realistic synthetic hyperspectral data, generated according to the two considered nonlinear mixing models, and also on two real hyperspectral images, are carried out to evaluate the performance of the proposed approaches. The obtained results show that the best proposed approaches yield a much better performance than various tested literature methods. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Figure 1

Article
Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions
Remote Sens. 2021, 13(8), 1532; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081532 - 15 Apr 2021
Viewed by 400
Abstract
Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new [...] Read more.
Although hyperspectral images capture very detailed information about the scanned objects, their efficient analysis, transfer, and storage are still important practical challenges due to their large volume. Classifying and segmenting such imagery are the pivotal steps in virtually all applications, hence developing new techniques for these tasks is a vital research area. Here, deep learning has established the current state of the art. However, deploying large-capacity deep models on-board an Earth observation satellite poses additional technological challenges concerned with their memory footprints, energy consumption requirements, and robustness against varying-quality image data, with the last problem being under-researched. In this paper, we tackle this issue, and propose a set of simulation scenarios that reflect a range of atmospheric conditions and noise contamination that may ultimately happen on-board an imaging satellite. We verify their impact on the generalization capabilities of spectral and spectral-spatial convolutional neural networks for hyperspectral image segmentation. Our experimental analysis, coupled with various visualizations, sheds more light on the robustness of the deep models and indicate that specific noise distributions can significantly deteriorate their performance. Additionally, we show that simulating atmospheric conditions is key to obtaining the learners that generalize well over image data acquired in different imaging settings. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Figure 1

Article
Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System
Remote Sens. 2021, 13(4), 583; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13040583 - 06 Feb 2021
Viewed by 684
Abstract
At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the [...] Read more.
At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the spectral-spatial joint classification of HSI based on the broad learning system (BLS) (SSBLS) method was proposed in this paper; it consists of three parts. Firstly, the Gaussian filter is adopted to smooth each band of the original spectra based on the spatial information to remove the noise. Secondly, the test sample’s labels can be obtained using the optimal BLS classification model trained with the spectral features smoothed by the Gaussian filter. At last, the guided filter is performed to correct the BLS classification results based on the spatial contextual information for improving the classification accuracy. Experiment results on the three real HSI datasets demonstrate that the mean overall accuracies (OAs) of ten experiments are 99.83% on the Indian Pines dataset, 99.96% on the Salinas dataset, and 99.49% on the Pavia University dataset. Compared with other methods, the proposed method in the paper has the best performance. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
CSR-Net: Camera Spectral Response Network for Dimensionality Reduction and Classification in Hyperspectral Imagery
Remote Sens. 2020, 12(20), 3294; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12203294 - 10 Oct 2020
Viewed by 802
Abstract
Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an [...] Read more.
Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an HSI classification architecture called camera spectral response network (CSR-Net), which can learn the optimal camera spectral response (CSR) function for HSI classification problems and effectively reduce the spectral dimensions of HSI. Specifically, we design a convolutional layer to simulate the capturing process of cameras, which learns the optimal CSR function for HSI classification. Then, spectral and spatial features are further extracted by spectral and spatial attention modules. On one hand, the learned CSR can be implemented physically and directly used to capture scenes, which makes the image acquisition process more convenient. On the other hand, compared with ordinary HSIs, we only need images with far fewer bands, without sacrificing the classification precision and avoiding the curse of dimensionality. The experimental results of four popular public hyperspectral datasets show that our method, with only a few image bands, outperforms state-of-the-art HSI classification methods which utilize the full spectral bands of images. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Blind Unmixing of Hyperspectral Remote Sensing Data: A New Geometrical Method Based on a Two-Source Sparsity Constraint
Remote Sens. 2020, 12(19), 3198; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12193198 - 30 Sep 2020
Viewed by 799
Abstract
Blind source separation (or unmixing) methods process a set of mixed signals, which are typically linear memoryless combinations of source signals, so as to estimate these unknown source signals and/or combination coefficients. These methods have been extensively applied to hyperspectral images in the [...] Read more.
Blind source separation (or unmixing) methods process a set of mixed signals, which are typically linear memoryless combinations of source signals, so as to estimate these unknown source signals and/or combination coefficients. These methods have been extensively applied to hyperspectral images in the field of remote sensing, because the reflectance spectrum of each image pixel is often a mixture of elementary contributions, due to the limited spatial resolution of hyperspectral remote sensing sensors. Each spatial source signal then corresponds to a pure material, and its value in each pixel is equal to the “abundance fraction” of the corresponding Earth surface covered by that pure material. The mixing coefficients then form the pure material spectra. Various unmixing methods have been designed for this data model and the majority of them are either geometrical or statistical, or even based on sparse regressions. Various such unmixing techniques mainly consider assumptions that are related to the presence or absence of pure pixels (i.e., pixels which contain only one pure material). The case when, for each pure material, the image includes at least one pixel or zone which only contains that material yielded attractive unmixing methods, but corresponds to a stringent sparsity condition. We here aim at relaxing that condition, by only requesting a few tiny pixel zones to contain two pure materials. The proposed linear and geometrical sparse-based, blind (or unsupervised) unmixing method first automatically detects these zones. Each such zone defines a line in the data representation space. These lines are then estimated and clustered. The pairs of cluster centers, corresponding to lines, which have an intersection, yield the spectra of pure materials, forming the columns of the mixing matrix. Finally, the proposed method derives all abundance fractions, i.e., source signals, by using a least squares method with a non-negativity constraint. This method is applied to realistic synthetic images and is shown to outperform various methods from the literature. Indeed, and for the conducted experiments, when considering the pure material spectra extraction, the obtained improvements, for the considered spectral angle mapper performance criterion, vary between 0.02 and 12.43. For the abundance fractions estimation, the proposed technique is able to achieve a normalized mean square error lower than 0.01%, while the tested literature methods yield errors greater than 0.1%. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Using a Panchromatic Image to Improve Hyperspectral Unmixing
Remote Sens. 2020, 12(17), 2834; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12172834 - 01 Sep 2020
Viewed by 989
Abstract
Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at [...] Read more.
Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at least one pure pixel for each material. This work presents a new approach aiming to overcome some of these limitations by introducing a co-registered panchromatic image in the unmixing process. Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based on a heterogeneity criterion applied on the panchromatic image followed by a spectral clustering. Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. The performance of our method, in regards of several criteria, is compared to those of state-of-the-art methods obtained on synthetic and satellite data describing urban and periurban scenes, and considering the French HYPXIM/HYPEX2 mission characteristics. The synthetic images are built with real spectral reflectances and do not contain a pure pixel for each endmember. The satellite images are simulated from airborne acquisition with the spatial and spectral features of the mission. Our method demonstrates the benefit of a panchromatic image to reduce some well-known limitations in unmixing hyperspectral data. On synthetic data, our method reduces the spectral angle between the endmembers and the real material spectra by 46% compared to the Vertex Component Analysis (VCA) and N-finder (N-FINDR) methods. On real data, HBEE-LCNMF and other methods yield equivalent performance, but, the proposed method shows more robustness over the data sets compared to the tested state-of-the-art methods. Moreover, HBEE-LCNMF does not require one to know the number of endmembers. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
A Developed Siamese CNN with 3D Adaptive Spatial-Spectral Pyramid Pooling for Hyperspectral Image Classification
Remote Sens. 2020, 12(12), 1964; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12121964 - 18 Jun 2020
Cited by 5 | Viewed by 890
Abstract
Since hyperspectral images (HSI) captured by different sensors often contain different number of bands, but most of the convolutional neural networks (CNN) require a fixed-size input, the generalization capability of deep CNNs to use heterogeneous input to achieve better classification performance has become [...] Read more.
Since hyperspectral images (HSI) captured by different sensors often contain different number of bands, but most of the convolutional neural networks (CNN) require a fixed-size input, the generalization capability of deep CNNs to use heterogeneous input to achieve better classification performance has become a research focus. For classification tasks with limited labeled samples, the training strategy of feeding CNNs with sample-pairs instead of single sample has proven to be an efficient approach. Following this strategy, we propose a Siamese CNN with three-dimensional (3D) adaptive spatial-spectral pyramid pooling (ASSP) layer, called ASSP-SCNN, that takes as input 3D sample-pair with varying size and can easily be transferred to another HSI dataset regardless of the number of spectral bands. The 3D ASSP layer can also extract different levels of 3D information to improve the classification performance of the equipped CNN. To evaluate the classification and generalization performance of ASSP-SCNN, our experiments consist of two parts: the experiments of ASSP-SCNN without pre-training and the experiments of ASSP-SCNN-based transfer learning framework. Experimental results on three HSI datasets demonstrate that both ASSP-SCNN without pre-training and transfer learning based on ASSP-SCNN achieve higher classification accuracies than several state-of-the-art CNN-based methods. Moreover, we also compare the performance of ASSP-SCNN on different transfer learning tasks, which further verifies that ASSP-SCNN has a strong generalization capability. Full article
(This article belongs to the Special Issue Machine Learning and Pattern Analysis in Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Back to TopTop