Special Issue "Advanced Techniques for Spaceborne 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 March 2020).

Special Issue Editors

Prof. Yinnian Liu
E-Mail Website
Guest Editor
The 2nd Laboratory, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yutian Rd, Shanghai 200083, China
Interests: hyperspectral remote sensing; infrared and hyperspectral imaging technology; high precision calibration technology; remote sensing image applications; precision optical mechanical technologies; Imaging processing & inversion
Prof. Dr. Qian Du
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39759, USA
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dexin Sun
E-Mail Website
Guest Editor
The 2nd Laboratory, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yutian Rd, Shanghai 200083, China
Interests: hyperspectral remote sensing; sensors; information acquisition & processing circuit technology; photoelectric information processing; electronic system design
Prof. Dr. Weiwei Sun
E-Mail Website
Guest Editor
Department of Geography and Spatial Information Techniques, Ningbo University, 818 Fenghua Road, Ningbo 315201, China
Interests: remote sensing of coastal wetland; hyperspectral remote sensing of coastal zone
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Wang
E-Mail Website
Guest Editor
The 2nd Laboratory, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 500 Yutian Rd, Shanghai 200083, China
Interests: hyperspectral remote sensing; image processing; target recognition; geometric correction

Special Issue Information

Dear Colleagues,

Hyperspectral remote-sensing imaging has advantageous narrow-continuous spectral bands that make it highly desirable in various applications, e.g., precision agriculture, forestry protection, urban planning, and environment monitoring. Compared with the airborne hyperspectral sensors, spaceborne hyperspectral images are more urgent due to their distinguished advantages on large-scale observations from space to the Earth’s surface. To date, there have been several spaceborne hyperspectral sensors, such as the EO-1 Hyperion of USA, CHRIS of European Space Agency, and HJ-1A and GaoFen (GF)-5 visible short-wave infrared hyperspectral sensor of China. More spaceborne hyperspectral missions will be launched in the next few years, such as the EnMAP of Germany, the PRISMA of Italy, HyspIRI of USA, and CartoSat-3/3A/3B and ResourceSat-3 of India. The launch of these satellites will attract increasing attention from researchers all over the world on spaceborne hyperspectral imaging techniques. In this Special Issue, advanced techniques for spaceborne hyperspectral remote-sensing will be presented.

This Special Issue aims to collect articles addressing new design and processing of spaceborne hyperspectral remote-sensing images. We invite you to submit the most recent advancements in all relevant aspects, including but not limited to the following topics:

  1. The hardware design, manufacture, and calibration of hyperspectral images (including on-orbit and planned missions.)
  2. Advanced data pre-processing methods for hyperspectral images (relative radiometric correction, absolute radiometric correction, geometric correction, image denoising, removal of stripe noise, the removal of the etalon effect, the reconstruction of dead pixels, etc.)
  3. Data quality evaluation criteria and comparative analysis (the quality evaluation metric, the comparison with spaceborne hyperspectral products, etc.)
  4. Advanced hyperspectral image processing methods and tools (data fusion, image segmentation and classification, change detection and multi-temporal analysis, target detection, and dimensionality reduction, etc.)
  5. Data quality verification from realistic applications (coastal environmental studies, precision agriculture and urban planning, etc.)

Dr. Yinnian Liu
Dr. Qian Du
Dr. Dexin Sun
Dr. Weiwei Sun
Dr. Feng Wang
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 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

  • hyperspectral imagery
  • sensors
  • remote sensing
  • high-precision calibration
  • image classification
  • data pre-processing
  • image denoising
  • remote-sensing image applications

Published Papers (11 papers)

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

Research

Article
HTD-Net: A Deep Convolutional Neural Network for Target Detection in Hyperspectral Imagery
Remote Sens. 2020, 12(9), 1489; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091489 - 07 May 2020
Cited by 9 | Viewed by 1348
Abstract
In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection [...] Read more.
In recent years, deep learning has dramatically improved the cognitive ability of the network by extracting depth features, and has been successfully applied in the field of feature extraction and classification of hyperspectral images. However, it is facing great difficulties for target detection due to extremely limited available labeled samples that are insufficient to train deep networks. In this paper, a novel target detection framework for deep learning is proposed, denoted as HTD-Net. To overcome the few-training-sample issue, the proposed framework utilizes an improved autoencoder (AE) to generate target signatures, and then finds background samples which differ significantly from target samples based on a linear prediction (LP) strategy. Then, the obtained target and background samples are used to enlarge the training set by generating pixel-pairs, which is viewed as the input of a pre-designed network architecture to learn discriminative similarity. During testing, pixel-pairs of a pixel to be labeled are constructed with both available target samples and background samples. Spectral difference between these pixel-pairs is classified by the well-trained network with results of similarity measurement. The outputs from a two-branch averaged similarity scores are combined to generate the final detection. Experimental results with several real hyperspectral data demonstrate the superiority of the proposed algorithm compared to some traditional target detectors. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?
Remote Sens. 2020, 12(5), 882; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12050882 - 09 Mar 2020
Cited by 10 | Viewed by 1877
Abstract
The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, [...] Read more.
The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Global and Local Tensor Sparse Approximation Models for Hyperspectral Image Destriping
Remote Sens. 2020, 12(4), 704; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040704 - 20 Feb 2020
Cited by 3 | Viewed by 996
Abstract
This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness [...] Read more.
This paper presents a global and local tensor sparse approximation (GLTSA) model for removing the stripes in hyperspectral images (HSIs). HSIs can easily be degraded by unwanted stripes. Two intrinsic characteristics of the stripes are (1) global sparse distribution and (2) local smoothness along the stripe direction. Stripe-free hyperspectral images are smooth in spatial domain, with strong spectral correlation. Existing destriping approaches often do not fully investigate such intrinsic characteristics of the stripes in spatial and spectral domains simultaneously. Those methods may generate new artifacts in extreme areas, causing spectral distortion. The proposed GLTSA model applies two 0 -norm regularizers to the stripe components and along-stripe gradient to improve the destriping performance. Two 1 -norm regularizers are applied to the gradients of clean image in spatial and spectral domains. The double non-convex functions in GLTSA are converted to single non-convex function by mathematical program with equilibrium constraints (MPEC). Experiment results demonstrate that GLTSA is effective and outperforms existing competitive matrix-based and tensor-based destriping methods in visual, as well as quantitative, evaluation measures. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong
Remote Sens. 2020, 12(4), 656; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040656 - 17 Feb 2020
Cited by 14 | Viewed by 1329
Abstract
Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), [...] Read more.
Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery
Remote Sens. 2019, 11(20), 2414; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202414 - 18 Oct 2019
Cited by 3 | Viewed by 819
Abstract
Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality [...] Read more.
Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality of HSI data and preserve the useful intrinsic information but they ignore the multi-manifold structure in hyperspectral image. In this paper, a novel dimensionality reduction (DR) method called spatial-spectral multiple manifold discriminant analysis (SSMMDA) was proposed for HSI classification. At first, several subsets are obtained from HSI data according to the prior label information. Then, a spectral-domain intramanifold graph is constructed for each submanifold to preserve the local neighborhood structure, a spatial-domain intramanifold scatter matrix and a spatial-domain intermanifold scatter matrix are constructed for each sub-manifold to characterize the within-manifold compactness and the between-manifold separability, respectively. Finally, a spatial-spectral combined objective function is designed for each submanifold to obtain an optimal projection and the discriminative features on different submanifolds are fused to improve the classification performance of HSI data. SSMMDA can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI. Experiments on three public HSI data sets demonstrate that the proposed SSMMDA method can achieve better classification accuracies in comparison with many state-of-the-art methods. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification
Remote Sens. 2019, 11(20), 2363; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11202363 - 11 Oct 2019
Cited by 10 | Viewed by 1116
Abstract
Deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3D convolutional [...] Read more.
Deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3D convolutional neural network (CNN) with a convolutional long short-term memory (CLSTM) network, called SSCC, is proposed herein for HSI classification. The SSCC framework employs cascaded 3D CNN to learn the spectral-spatial features of HSIs and uses the CLSTM network to extract sequence features. Residual connections are used in SSCC to accelerate model convergence, with the outputs of previous convolutional layers concatenated as inputs for subsequent layers. Moreover, the data augmentation, parametric rectified linear unit, dynamic learning rate, batch normalization, and regularization (including dropout and L2) methods are used to increase classification accuracy and prevent overfitting. These attributes allow the SSCC framework to achieve good performance for HSI classification within 20 epochs. Three well-known datasets including Indiana Pines, University of Pavia, and Pavia Center were employed to evaluate the classification performance of the proposed algorithm. The GF-5 dataset of Anxin County, obtained from China’s recently launched spaceborne Advanced Hyperspectral Imager, was also used for classification experiments. The experimental results demonstrate that the proposed SSCC framework achieves state-of-the-art performance with better training efficiency than other deep learning methods. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Hyperspectral Image Denoising Using Global Weighted Tensor Norm Minimum and Nonlocal Low-Rank Approximation
Remote Sens. 2019, 11(19), 2281; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11192281 - 29 Sep 2019
Cited by 9 | Viewed by 1214
Abstract
A hyperspectral image (HSI) contains abundant spatial and spectral information, but it is always corrupted by various noises, especially Gaussian noise. Global correlation (GC) across spectral domain and nonlocal self-similarity (NSS) across spatial domain are two important characteristics for an HSI. To keep [...] Read more.
A hyperspectral image (HSI) contains abundant spatial and spectral information, but it is always corrupted by various noises, especially Gaussian noise. Global correlation (GC) across spectral domain and nonlocal self-similarity (NSS) across spatial domain are two important characteristics for an HSI. To keep the integrity of the global structure and improve the details of the restored HSI, we propose a global and nonlocal weighted tensor norm minimum denoising method which jointly utilizes GC and NSS. The weighted multilinear rank is utilized to depict the GC information. To preserve structural information with NSS, a patch-group-based low-rank-tensor-approximation (LRTA) model is designed. The LRTA makes use of Tucker decompositions of 4D patches, which are composed of a similar 3D patch group of HSI. The alternating direction method of multipliers (ADMM) is adapted to solve the proposed models. Experimental results show that the proposed algorithm can preserve the structural information and outperforms several state-of-the-art denoising methods. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction
Remote Sens. 2019, 11(15), 1822; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11151822 - 04 Aug 2019
Cited by 2 | Viewed by 1290
Abstract
Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in [...] Read more.
Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in the field of hyperspectral images processing. In general, a desirable low dimensionality feature representation should be discriminative and compact. To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper. In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative. Furthermore, TDA-CFR redefines the feature representation of each spectral band by employing the tensor low rank decomposition framework which leads to a more compact representation. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Figure 1

Article
Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation
Remote Sens. 2019, 11(11), 1318; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11111318 - 01 Jun 2019
Cited by 12 | Viewed by 1324
Abstract
Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various [...] Read more.
Most of the conventional anomaly detectors only take advantage of the spectral information and do not consider the spatial information within neighboring pixels. Recently, the spectral-spatial based local summation anomaly detection (LSAD) algorithm has achieved excellent detection performances. In order to obtain various local spatial distributions with the neighboring pixels of the pixels under test, the LSAD algorithm exploits a multiple-window sliding filter, which can be computationally expensive and time-consuming. In this paper, to address these issues, two modified LSAD-based methods are proposed. The first method, called local summation unsupervised nearest regularized subspace with an outlier removal anomaly detector (LSUNRSORAD), is based on the concept that each pixel in the background can be approximately represented by its spatial neighborhood. The second method, called local summation anomaly detection based on collaborative representation and inverse distance weight (LSAD-CR-IDW), uses the surrounding pixels collected inside the outer window, while outside the inner window, to linearly represent the test pixel and introduces collaborative representation and inverse distance weight to further improve the computational speed and detection precision, respectively. The proposed methods were applied to a synthetic dataset and three real datasets. The experimental results show that the proposed methods have a better detection accuracy and computational speed when compared with the LSAD algorithm and others. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
Spatial–Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification
Remote Sens. 2019, 11(7), 884; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11070884 - 11 Apr 2019
Cited by 25 | Viewed by 2513
Abstract
Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial [...] Read more.
Jointly using spectral and spatial information has become a mainstream strategy in the field of hyperspectral image (HSI) processing, especially for classification. However, due to the existence of noisy or correlated spectral bands in the spectral domain and inhomogeneous pixels in the spatial neighborhood, HSI classification results are often degraded and unsatisfactory. Motivated by the attention mechanism, this paper proposes a spatial–spectral squeeze-and-excitation (SSSE) module to adaptively learn the weights for different spectral bands and for different neighboring pixels. The SSSE structure can suppress or motivate features at a certain position, which can effectively resist noise interference and improve the classification results. Furthermore, we embed several SSSE modules into a residual network architecture and generate an SSSE-based residual network (SSSERN) model for HSI classification. The proposed SSSERN method is compared with several existing deep learning networks on two benchmark hyperspectral data sets. Experimental results demonstrate the effectiveness of our proposed network. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Graphical abstract

Article
A Novel FPGA-Based Architecture for Fast Automatic Target Detection in Hyperspectral Images
Remote Sens. 2019, 11(2), 146; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020146 - 14 Jan 2019
Cited by 8 | Viewed by 2193
Abstract
Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. [...] Read more.
Onboard target detection of hyperspectral imagery (HSI), considered as a significant remote sensing application, has gained increasing attention in the latest years. It usually requires processing huge volumes of HSI data in real-time under constraints of low computational complexity and high detection accuracy. Automatic target generation process based on an orthogonal subspace projector (ATGP-OSP) is a well-known automatic target detection algorithm, which is widely used owing to its competitive performance. However, ATGP-OSP has an issue to be deployed onboard in real-time target detection due to its iteratively calculating the inversion of growing matrices and increasing matrix multiplications. To resolve this dilemma, we propose a novel fast implementation of ATGP (Fast-ATGP) while maintaining target detection accuracy of ATGP-OSP. Fast-ATGP takes advantage of simple regular matrix add/multiply operations instead of increasingly complicated matrix inversions to update growing orthogonal projection operator matrices. Furthermore, the updated orthogonal projection operator matrix is replaced by a normalized vector to perform the inner-product operations with each pixel for finding a target per iteration. With these two major optimizations, the computational complexity of ATGP-OSP is substantially reduced. What is more, an FPGA-based implementation of the proposed Fast-ATGP using high-level synthesis (HLS) is developed. Specifically, an efficient architecture containing a bunch of pipelines being executed in parallel is further designed and evaluated on a Xilinx XC7VX690T FPGA. The experimental results demonstrate that our proposed FPGA-based Fast-ATGP is able to automatically detect multiple targets on a commonly used dataset (AVIRIS Cuprite Data) at a high-speed rate of 200 MHz with a significant speedup of nearly 34.3 times that of ATGP-OSP, while retaining nearly the same high detection accuracy. Full article
(This article belongs to the Special Issue Advanced Techniques for Spaceborne Hyperspectral Remote Sensing)
Show Figures

Figure 1

Back to TopTop