Hyperspectral Imaging and Signal Processing

Abstract submission deadline

31 January 2025

Manuscript submission deadline

31 March 2025

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Topic Information

Dear Colleagues,

Hyperspectral imaging technology has been used for Earth observation for more than four decades. A variety of hyperspectral sensors based on different platforms, such as satellites, aircraft, drones, cars, and ground, have been developed, which have greatly promoted the applications of hyperspectral imaging technology. With the development of computer science and the availability of hyperspectral datasets, more and more artificial intelligence algorithms based on machine learning or deep learning are used for the processing and application of hyperspectral imagery. In addition, some potential applications using hyperspectral imaging technology are investigated with the improvement of the spatial and spectral resolution of hyperspectral sensors. In this Topic, we focus on hyperspectral sensor design, data processing, and applications. Authors are encouraged to submit contributions that report original studies on hyperspectral imaging and signal processing. If your research is limited by spaceborne hyperspectral images, you can contact the Topic editor team to seek data support. Topics will include but not be limited to:

• Hyperspectral sensor design, development, and performance valuation;
• Key techniques in sub-systems, including optics, electronics, mechanics, and detectors;
• Hyperspectral data preprocessing and image quality improvement, e.g., radiometric calibration, geometric correction, and atmosphere correction;
• Innovative algorithm development for hyperspectral remote sensing;
• Data fusion of multiple remote sensing sources;
• Hyperspectral LiDAR;
• Hyperspectral applications in the ocean, forests, agriculture, environment, minerals and oil, etc.

Prof. Dr. Yinnian Liu
Prof. Dr. Bing Zhang
Prof. Dr. Liangpei Zhang
Dr. Chenchao Xiao
Prof. Dr. Yueming Wang
Prof. Dr. Yongguang Zhang
Prof. Dr. Qingli Li
Dr. Yuwei Chen
Dr. Jianxin Jia
Dr. Mingyang Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Drones drones	4.8	6.1	2017	17.9 Days	CHF 2600	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Land land	3.9	3.7	2012	14.8 Days	CHF 2600	Submit
Photonics photonics	2.4	2.3	2014	15.5 Days	CHF 2400	Submit
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700	Submit
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600	Submit

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Published Papers (15 papers)

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18 pages, 9402 KiB  

Open AccessArticle

Transfer Learning-Based Hyperspectral Image Classification Using Residual Dense Connection Networks

by Hao Zhou, Xianwang Wang, Kunming Xia, Yi Ma and Guowu Yuan

Sensors 2024, 24(9), 2664; https://doi.org/10.3390/s24092664 - 23 Apr 2024

Viewed by 329

Abstract

The extraction of effective classification features from high-dimensional hyperspectral images, impeded by the scarcity of labeled samples and uneven sample distribution, represents a formidable challenge within hyperspectral image classification. Traditional few-shot learning methods confront the dual dilemma of limited annotated samples and the [...] Read more.

The extraction of effective classification features from high-dimensional hyperspectral images, impeded by the scarcity of labeled samples and uneven sample distribution, represents a formidable challenge within hyperspectral image classification. Traditional few-shot learning methods confront the dual dilemma of limited annotated samples and the necessity for deeper, more effective features from complex hyperspectral data, often resulting in suboptimal outcomes. The prohibitive cost of sample annotation further exacerbates the challenge, making it difficult to rely on a scant number of annotated samples for effective feature extraction. Prevailing high-accuracy algorithms require abundant annotated samples and falter in deriving deep, discriminative features from limited data, compromising classification performance for complex substances. This paper advocates for an integration of advanced spectral–spatial feature extraction with meta-transfer learning to address the classification of hyperspectral signals amidst insufficient labeled samples. Initially trained on a source domain dataset with ample labels, the model undergoes transference to a target domain with minimal samples, utilizing dense connection blocks and tree-dimensional convolutional residual connections to enhance feature extraction and maximize spatial and spectral information retrieval. This approach, validated on three diverse hyperspectral datasets—IP, UP, and Salinas—significantly surpasses existing classification algorithms and small-sample techniques in accuracy, demonstrating its applicability to high-dimensional signal classification under label constraints. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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21 pages, 2495 KiB  

Open AccessArticle

Hyperspectral Image Classification Using Multi-Scale Lightweight Transformer

by Quan Gu, Hongkang Luan, Kaixuan Huang and Yubao Sun

Electronics 2024, 13(5), 949; https://doi.org/10.3390/electronics13050949 - 29 Feb 2024

Viewed by 484

Abstract

The distinctive feature of hyperspectral images (HSIs) is their large number of spectral bands, which allows us to identify categories of ground objects by capturing discrepancies in spectral information. Convolutional neural networks (CNN) with attention modules effectively improve the classification accuracy of HSI. [...] Read more.

The distinctive feature of hyperspectral images (HSIs) is their large number of spectral bands, which allows us to identify categories of ground objects by capturing discrepancies in spectral information. Convolutional neural networks (CNN) with attention modules effectively improve the classification accuracy of HSI. However, CNNs are not successful in capturing long-range spectral–spatial dependence. In recent years, Vision Transformer (VIT) has received widespread attention due to its excellent performance in acquiring long-range features. However, it requires calculating the pairwise correlation between token embeddings and has the complexity of the square of the number of tokens, which leads to an increase in the computational complexity of the network. In order to cope with this issue, this paper proposes a multi-scale spectral–spatial attention network with frequency-domain lightweight Transformer (MSA-LWFormer) for HSI classification. This method synergistically integrates CNN, attention mechanisms, and Transformer into the spectral–spatial feature extraction module and frequency-domain fused classification module. Specifically, the spectral–spatial feature extraction module employs a multi-scale 2D-CNN with multi-scale spectral attention (MS-SA) to extract the shallow spectral–spatial features and capture the long-range spectral dependence. In addition, The frequency-domain fused classification module designs a frequency-domain lightweight Transformer that employs the Fast Fourier Transform (FFT) to convert features from the spatial domain to the frequency domain, effectively extracting global information and significantly reducing the time complexity of the network. Experiments on three classic hyperspectral datasets show that MSA-LWFormer has excellent performance. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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12 pages, 1919 KiB  

Open AccessCommunication

Spatial-Spectral BERT for Hyperspectral Image Classification

by Mahmood Ashraf, Xichuan Zhou, Gemine Vivone, Lihui Chen, Rong Chen and Reza Seifi Majdard

Remote Sens. 2024, 16(3), 539; https://doi.org/10.3390/rs16030539 - 31 Jan 2024

Viewed by 919

Abstract

Several deep learning and transformer models have been recommended in previous research to deal with the classification of hyperspectral images (HSIs). Among them, one of the most innovative is the bidirectional encoder representation from transformers (BERT), which applies a distance-independent approach to capture [...] Read more.

Several deep learning and transformer models have been recommended in previous research to deal with the classification of hyperspectral images (HSIs). Among them, one of the most innovative is the bidirectional encoder representation from transformers (BERT), which applies a distance-independent approach to capture the global dependency among all pixels in a selected region. However, this model does not consider the local spatial-spectral and spectral sequential relations. In this paper, a dual-dimensional (i.e., spatial and spectral) BERT (the so-called D²BERT) is proposed, which improves the existing BERT model by capturing more global and local dependencies between sequential spectral bands regardless of distance. In the proposed model, two BERT branches work in parallel to investigate relations among pixels and spectral bands, respectively. In addition, the layer intermediate information is used for supervision during the training phase to enhance the performance. We used two widely employed datasets for our experimental analysis. The proposed D²BERT shows superior classification accuracy and computational efficiency with respect to some state-of-the-art neural networks and the previously developed BERT model for this task. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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24 pages, 1764 KiB  

Open AccessArticle

Hyperspectral Image Shadow Enhancement Using Three-Dimensional Dynamic Stochastic Resonance and Classification Based on ResNet

by Xuefeng Liu, Yangyang Kou and Min Fu

Electronics 2024, 13(3), 500; https://doi.org/10.3390/electronics13030500 - 24 Jan 2024

Cited by 1 | Viewed by 560

Abstract

Classification is an important means of extracting rich information from hyperspectral images (HSIs). However, many HSIs contain shadowed areas, where noise severely affects the extraction of useful information. General noise removal may lead to loss of spatial correlation and spectral features. In contrast, [...] Read more.

Classification is an important means of extracting rich information from hyperspectral images (HSIs). However, many HSIs contain shadowed areas, where noise severely affects the extraction of useful information. General noise removal may lead to loss of spatial correlation and spectral features. In contrast, dynamic stochastic resonance (DSR) converts noise into capability that enhances the signal in a way that better preserves the image’s original information. Nevertheless, current one-dimensional and 2D DSR methods fail to fully utilize the tensor properties of hyperspectral data and preserve the complete spectral features. Therefore, a hexa-directional differential format is derived in this paper to solve the system’s output, and the iterative equation for HSI shadow enhancement is obtained, enabling 3D parallel processing of HSI spatial–spectral information. Meanwhile, internal parameters are adjusted to achieve optimal resonance. Furthermore, the residual neural network 152 model embedded with the convolutional block attention module is proposed to diminish information redundancy and leverage data concealed within shadow areas. Experimental results on a real-world HSI demonstrate the potential performance of 3D DSR in enhancing weak signals in HSI shadow regions and the proposed approach’s effectiveness in improving classification. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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20 pages, 9554 KiB  

Open AccessArticle

A U-Shaped Convolution-Aided Transformer with Double Attention for Hyperspectral Image Classification

by Ruiru Qin, Chuanzhi Wang, Yongmei Wu, Huafei Du and Mingyun Lv

Remote Sens. 2024, 16(2), 288; https://doi.org/10.3390/rs16020288 - 11 Jan 2024

Viewed by 706

Abstract

Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates [...] Read more.

Convolutional neural networks (CNNs) and transformers have achieved great success in hyperspectral image (HSI) classification. However, CNNs are inefficient in establishing long-range dependencies, and transformers may overlook some local information. To overcome these limitations, we propose a U-shaped convolution-aided transformer (UCaT) that incorporates convolutions into a novel transformer architecture to aid classification. The group convolution is employed as parallel local descriptors to extract detailed features, and then the multi-head self-attention recalibrates these features in consistent groups, emphasizing informative features while maintaining the inherent spectral–spatial data structure. Specifically, three components are constructed using particular strategies. First, the spectral groupwise self-attention (spectral-GSA) component is developed for spectral attention, which selectively emphasizes diagnostic spectral features among neighboring bands and reduces the spectral dimension. Then, the spatial dual-scale convolution-aided self-attention (spatial-DCSA) encoder and spatial convolution-aided cross-attention (spatial-CCA) decoder form a U-shaped architecture for per-pixel classifications over HSI patches, where the encoder utilizes a dual-scale strategy to explore information in different scales and the decoder adopts the cross-attention for information fusion. Experimental results on three datasets demonstrate that the proposed UCaT outperforms the competitors. Additionally, a visual explanation of the UCaT is given, showing its ability to build global interactions and capture pixel-level dependencies. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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24 pages, 7871 KiB  

Open AccessArticle

An Optimized Inversion Method for Hyperspectral Image Fusion Based on a Hue–Intensity–Saturation, Wavelet, and Trust-Region Conjugate Gradient Method

by Jiangbo Wu, Aiming Ge, Shuo Liu, Qiuyang Wang, Dongsheng Zhu and Xindi Chen

Electronics 2024, 13(2), 252; https://doi.org/10.3390/electronics13020252 - 05 Jan 2024

Viewed by 706

Abstract

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods [...] Read more.

In hyperspectral remote sensing, achieving high spatial resolution holds paramount importance for an array of applications, such as environmental monitoring, geographic mapping, and precision agriculture. Nevertheless, conventional hyperspectral images frequently grapple with the issue of restricted spatial resolution. We apply optimized inversion methods to hyperspectral image fusion and present an innovative approach for hyperspectral image fusion which combines the Hue–Intensity–Saturation (HIS) transform, the wavelet transform, and the Trust-Region Conjugate Gradient technique. This amalgamation not only refines spatial precision but also augments spectral faithfulness, which is a pivotal aspect for applications like precise object detection and classification. In the context of our investigation, we conducted a thorough validation of our proposed HIS, Wavelet, and Trust-Region Conjugate Gradient (TRCG-HW) method for image fusion using a comprehensive suite of evaluation metrics. These metrics encompassed the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation Coefficient (CC), Spectral Angle Mapper (SAM), and Error Relative Global Accuracy Score (ERGAS). The findings incontrovertibly establish TRCG-HW as the preeminent method among those considered. Our study effectively tackles the pressing predicament of low spatial resolution encountered in hyperspectral imaging. This innovative paradigm harbors the potential to revolutionize high-resolution hyperspectral data acquisition, propelling the field of hyperspectral remote sensing forward and efficiently catering to crucial application. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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17 pages, 21887 KiB  

Open AccessArticle

A Neural Network for Hyperspectral Image Denoising by Combining Spatial–Spectral Information

by Xiaoying Lian, Zhonghai Yin, Siwei Zhao, Dandan Li, Shuai Lv, Boyu Pang and Dexin Sun

Remote Sens. 2023, 15(21), 5174; https://doi.org/10.3390/rs15215174 - 30 Oct 2023

Viewed by 1038

Abstract

Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions is a challenging task in hyperspectral data processing. Existing methods typically [...] Read more.

Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions is a challenging task in hyperspectral data processing. Existing methods typically focus on specific types of noise, resulting in limited applicability and an inadequate ability to handle complex noise scenarios. This paper proposes a denoising method based on a network that considers both the spatial structure and spectral differences of noise in an image data cube. The proposed network takes into account the DN value of the current band, as well as the horizontal, vertical, and spectral gradients as inputs. A multi-resolution convolutional module is employed to accurately extract spatial and spectral noise features, which are then aggregated through residual connections at different levels. Finally, the residual mixed noise is approximated. Both simulated and real case studies confirm the effectiveness of the proposed denoising method. In the simulation experiment, the average PSNR value of the denoised results reached 31.47 at a signal-to-noise ratio of 8 dB, and the experimental results on the real data set Indian Pines show that the classification accuracy of the denoised hyperspectral image (HSI) is improved by 16.31% compared to the original noisy version. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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22 pages, 12136 KiB  

Open AccessArticle

The Use of a Stable Super-Resolution Generative Adversarial Network (SSRGAN) on Remote Sensing Images

by Boyu Pang, Siwei Zhao and Yinnian Liu

Remote Sens. 2023, 15(20), 5064; https://doi.org/10.3390/rs15205064 - 22 Oct 2023

Viewed by 1023

Abstract

Because of the complicated imaging conditions in space and the finite imaging systems on satellites, the resolution of remote sensing images is limited. The process of increasing an image’s resolution, image super-resolution, aims to obtain a clearer image. High-resolution (HR) images are affected [...] Read more.

Because of the complicated imaging conditions in space and the finite imaging systems on satellites, the resolution of remote sensing images is limited. The process of increasing an image’s resolution, image super-resolution, aims to obtain a clearer image. High-resolution (HR) images are affected by various input conditions, such as motion, imaging blur, down-sampling matrix, and various types of noise. Changes in these conditions seriously affect low-resolution (LR) images, so if the imaging process is a pathological problem, super-resolution reconstruction is a pathological anti-problem. To optimize the imaging quality of satellites without changing the optical system, we chose to reconstruct images acquired by satellites using deep learning. We changed the original super-resolution generative adversarial nets network, upgraded the generator’s network part to ResNet-50, and inserted an additional fully connected (FC) layer in the network of the discriminator part. We also modified the loss function by changing the weight of regularization loss from 2 × 10⁻⁸ to 2 × 10⁻⁹, aiming to preserve more detail. In addition, we carefully and specifically chose remote sensing images taken under low-light circumstances from GF-5 satellites to form a new dataset for training and validation. The test results proved that our method can obtain good results. The reconstruction peak signal-to-noise ratio (PSNR) at the scaling factors of 2, 3, and 4 reached 32.6847, 31.8191, and 30.5095 dB, respectively, and the corresponding structural similarity (SSIM) reached 0.8962, 0.8434, and 0.8124. The super-resolution speed was also satisfactory, making real-time reconstruction more probable. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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12 pages, 4761 KiB  

Open AccessCommunication

Pre-Launch Spectral Calibration of the Absorbed Aerosol Sensor

by Jinghua Mao, Yongmei Wang, Entao Shi and Jinduo Wang

Sensors 2023, 23(20), 8590; https://doi.org/10.3390/s23208590 - 20 Oct 2023

Viewed by 567

Abstract

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument’s spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the SRF is not a conventional Gaussian curve. To [...] Read more.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument’s spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the SRF is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which has a distribution similar to the flat-topped multi-Gaussian function, is employed to fit the measured data in a spectral calibration. According to the results, the SRF’s shapes resembling a Gaussian curve with a flat top could be derived, which contains a full width at half maximum (FWHM) of 1.78–1.82 nm for the AAS. The results show that the correlation is about 0.99, which indicates the usefulness of the fitting function that could better characterize the SRF of the instrument. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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24 pages, 10759 KiB  

Open AccessArticle

Impact of a Hyperspectral Satellite Cross-Calibration Radiometer’s Spatial and Noise Characteristics on Cross-Calibration

by Robert E. Ryan, Mary Pagnutti, Max Huggins, Kara Burch, David Sitton, Kimberly Manriquez and Hannah Ryan

Remote Sens. 2023, 15(18), 4419; https://doi.org/10.3390/rs15184419 - 08 Sep 2023

Cited by 1 | Viewed by 866

Abstract

The satellite cross-calibration radiometer (SCR) is a conceptual on-orbit hyperspectral imaging radiometer that transfers the radiometric calibration from a “gold-standard” reference instrument such as the Landsat 8/9 Operational Land Imager (OLI) to other civil, international, or commercial “client” multispectral satellite systems via near-simultaneous [...] Read more.

The satellite cross-calibration radiometer (SCR) is a conceptual on-orbit hyperspectral imaging radiometer that transfers the radiometric calibration from a “gold-standard” reference instrument such as the Landsat 8/9 Operational Land Imager (OLI) to other civil, international, or commercial “client” multispectral satellite systems via near-simultaneous cross-calibration acquisitions. The spectral resolution, spectral range, spatial resolution, and signal-to-noise ratio (SNR) all significantly impact the complexity and cost of hyperspectral SCRs, so it is important to understand their effect on cross-calibration quality. This paper discusses the results of a trade study to quantify the effects of varying ground sample distance (GSD), number of independent samples, and instrument/scene noise on cross-calibration gain uncertainties. The trade study used a simulated SCR cross-calibration with near-simultaneous nadir overpasses (SNOs) of the Landsat 8 OLI acting as the reference instrument and the DLR Earth Sensing Imaging Spectrometer (DESIS) acting as a surrogate SCR hyperspectral instrument. Results demonstrate that cross-calibration uncertainty is only minimally affected by spatial resolution and SNR, which may allow SCR instruments to be developed at a lower cost. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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16 pages, 3895 KiB  

Open AccessArticle

Spectral Image Reconstruction Using Recovered Basis Vector Coefficients

by Wei Xu, Liangzhuang Wei, Xiangwei Yi and Yandan Lin

Photonics 2023, 10(9), 1018; https://doi.org/10.3390/photonics10091018 - 06 Sep 2023

Viewed by 784

Abstract

Spectral imaging plays a crucial role in various fields, including remote sensing, medical imaging, and material analysis, but it often requires specialized and expensive equipment, making it inaccessible to many. Its application is also limited by the interdependent constraints of temporal, spatial, and [...] Read more.

Spectral imaging plays a crucial role in various fields, including remote sensing, medical imaging, and material analysis, but it often requires specialized and expensive equipment, making it inaccessible to many. Its application is also limited by the interdependent constraints of temporal, spatial, and spectral resolutions. In order to address these issues, and thus, obtain high-quality spectral images in a time-efficient and affordable manner, we proposed one two-step method for spectral image reconstruction from easily available RGB images under the down-sampling schemes. Specifically, we investigated how RGB values characterize spectral reflectance and found that, compared to the intuitive and straightforward RGB images themselves, their corresponding basis vector coefficients can represent the prior information of spectral images more explicitly and are better suited for spectral image reconstruction tasks. Thus, we derived one data-driven algebraic method to recover the corresponding basis vector coefficients from RGB images in an analytical form and then employed one CNN-based neural network to learn the patch-level mapping from the recovered basis vector coefficients to spectral images. To evaluate the effect of introducing the basis vector coefficient recovery step, several CNNs which typically perform well in spectral image reconstruction are chosen as benchmarks to compare the variation in reconstruction performance. Experimental results on a large public spectral image dataset and our real-world dataset demonstrate that compared to the unaltered version, those CNNs guided by the recovered basis vector coefficients can achieve significant performance improvement in the reconstruction accuracy. Furthermore, this method is plug-and-play, with very little computational performance consumption, thus maintaining a high speed of calculation. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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18 pages, 6899 KiB  

Open AccessArticle

Hyperspectral Channel-Modulated Static Birefringent Fourier Transform Imaging Spectropolarimeter with Zoomable Spectral Resolution

by Xiangzhe Zhang, Jingping Zhu, Liqing Huang, Yu Zhang, Huimin Wang, Haoxiang Li, Fengqi Guo and Jinxin Deng

Photonics 2023, 10(8), 950; https://doi.org/10.3390/photonics10080950 - 18 Aug 2023

Cited by 2 | Viewed by 795

Abstract

A novel channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS) is introduced, which is based on a double Wollaston prism (DWP). With an adjustable air gap (AG), the spectral resolution can be adjusted by changing the AG. The CSBFTIS combines the channel-modulated imaging [...] Read more.

A novel channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS) is introduced, which is based on a double Wollaston prism (DWP). With an adjustable air gap (AG), the spectral resolution can be adjusted by changing the AG. The CSBFTIS combines the channel-modulated imaging spectropolarimeter and the slit-free static birefringent Fourier transform imaging spectrometer technology with adjustable spectral resolution. The device is compact and robust, with a wide spectral range and a large luminous flux. Compared with various previous spectropolarimeters, it can greatly reduce the size of the spectral image data to adapt to different application requirements. A prototype is built, and simulation and experiments are carried out, and the results prove the effectiveness of the method. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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20 pages, 6306 KiB  

Open AccessArticle

Few-Shot Hyperspectral Image Classification Based on Convolutional Residuals and SAM Siamese Networks

by Mengen Xia, Guowu Yuan, Lingyu Yang, Kunming Xia, Ying Ren, Zhiliang Shi and Hao Zhou

Electronics 2023, 12(16), 3415; https://doi.org/10.3390/electronics12163415 - 11 Aug 2023

Cited by 5 | Viewed by 1133

Abstract

With the development of few-shot learning, significant progress has been achieved in hyperspectral image classification using related networks, leading to improved classification outcomes. However, practical few-shot hyperspectral image classification encounters challenges such as network overfitting and insufficient feature extraction during the model training [...] Read more.

With the development of few-shot learning, significant progress has been achieved in hyperspectral image classification using related networks, leading to improved classification outcomes. However, practical few-shot hyperspectral image classification encounters challenges such as network overfitting and insufficient feature extraction during the model training process. To address these issues, we propose a model called CRSSNet (Convolutional Residuals and SAM Siamese Networks) for few-shot hyperspectral image classification. In this model, we deepen the network depth and employ the convolutional residual technique to enhance the feature extraction capabilities and alleviate the problem of network gradient degradation. Additionally, we introduce the Spatial Attention Mechanism (SAM) to effectively leverage spatial information features in hyperspectral images. Lastly, metric learning is employed by comparing the distance between two output feature vectors to determine the label category. Experimental results demonstrate that our method achieves superior classification performance compared to other methods. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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17 pages, 9720 KiB  

Open AccessArticle

Thangka Hyperspectral Image Super-Resolution Based on a Spatial–Spectral Integration Network

by Sai Wang and Fenglei Fan

Remote Sens. 2023, 15(14), 3603; https://doi.org/10.3390/rs15143603 - 19 Jul 2023

Viewed by 1435

Abstract

Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral images (HSIs), especially for the preservation and analysis of [...] Read more.

Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral images (HSIs), especially for the preservation and analysis of Thangka cultural heritage. However, existing CNN-based methods encounter difficulties in effectively preserving spatial information, due to challenges such as registration errors and spectral variability. To overcome these limitations, we present a novel cross-sensor super-resolution (SR) framework that utilizes high-resolution RGBs (HR-RGBs) to enhance the spectral features in low-resolution hyperspectral images (LR-HSIs). Our approach utilizes spatial–spectral integration (SSI) blocks and spatial–spectral restoration (SSR) blocks to effectively integrate and reconstruct spatial and spectral features. Furthermore, we introduce a frequency multi-head self-attention (F-MSA) mechanism that treats high-, medium-, and low-frequency features as tokens, enabling self-attention computations across the frequency dimension. We evaluate our method on a custom dataset of ancient Thangka paintings and demonstrate its effectiveness in enhancing the spectral resolution in high-resolution hyperspectral images (HR-HSIs), while preserving the spatial characteristics of Thangka artwork with minimal information loss. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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21 pages, 12231 KiB  

Open AccessArticle

Spatial–Spectral Joint Hyperspectral Anomaly Detection Based on a Two-Branch 3D Convolutional Autoencoder and Spatial Filtering

by Shuai Lv, Siwei Zhao, Dandan Li, Boyu Pang, Xiaoying Lian and Yinnian Liu

Remote Sens. 2023, 15(10), 2542; https://doi.org/10.3390/rs15102542 - 12 May 2023

Cited by 1 | Viewed by 1270

Abstract

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by [...] Read more.

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial–spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial–spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods. Full article

(This article belongs to the Topic Hyperspectral Imaging and Signal Processing)

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