Hyperspectral Applications Research Articles

Wide band UV/Vis/NIR blazed-binary reflective gratings for spectro-imagers: two lithographic technologies investigation

We report on subwavelength reflective gratings for hyperspectral applications operating in a very large spectral band (340–1040 nm). Our study concerns a blazed-binary grating having a period of 30 μm and composed of 2D subwavelength structures with size from 120 nm to 350 nm. We demonstrate the manufacturing of the gratings on 3″ wafers by two lithography technologies (e-beam and nanoimprint) followed by classical dry etching process. Optical measurements show that the subwavelength grating approach enables a broadband efficiency, polarization behaviour and wavefront quality improvement with respect to the requirements for the next generation of spectro-imagers for Earth observation missions. An outlook towards spherical substrate based on nanoimprint lithography is also reported with the results of mixed features replication (holes and pillars in the range of 160–330 nm) on a 540 mm concave substrate which demonstrate uniformity and accuracy capabilities over 3″ surface.

Refinement method for compressive hyperspectral data cubes based on self-fusion.

Compressive hyperspectral images often suffer from various noises and artifacts, which severely degrade the imaging quality and limit subsequent applications. In this paper, we present a refinement method for compressive hyperspectral data cubes based on self-fusion of the raw data cubes, which can effectively reduce various noises and improve the spatial and spectral details of the data cubes. To verify the universality, flexibility, and extensibility of the self-fusion refinement (SFR) method, a series of specific simulations and practical experiments were conducted, and SFR processing was performed through different fusion algorithms. The visual and quantitative assessments of the results demonstrate that, in terms of noise reduction and spatial-spectral detail restoration, the SFR method generally is much better than other typical denoising methods for hyperspectral data cubes. The results also indicate that the denoising effects of SFR greatly depend on the fusion algorithm used, and SFR implemented by joint bilateral filtering (JBF) performs better than SRF by guided filtering (GF) or a Markov random field (MRF). The proposed SFR method can significantly improve the quality of a compressive hyperspectral data cube in terms of noise reduction, artifact removal, and spatial and spectral detail improvement, which will further benefit subsequent hyperspectral applications.

EVALUATION OF ZY1-02D HYPERSPECTRAL SATELLITE SURFACE REFLECTANCE PRODUCTS

Abstract. ZY-1 02D satellite is an important satellite in China space infrastructure planning. The Land Satellite Remote Sensing Application Center of the Ministry of Natural Resources of the People’s Republic of China (LASAC) undertook the construction of the satellite hyperspectral data processing system and produced surface reflectance products. Reflectance is the basis of hyperspectral remote sensing data application, which is directly related to the efficiency and quality of hyperspectral application. At present, there is no comprehensive and in-depth study on the accuracy evaluation of the atmospheric correction reflectance of ZY1 - 02D satellite hyperspectral data. In view of this problem, Accuracy Verification of LASAC products using accuracy, precision, uncertainty, spectral curve correlation and spectral angle chord four indicators based on surface reflectance produced by AERONET ground measured data. The results show that the average values of accuracy, precision, uncertainty of LASAC products are 3.2 %, 2.02 % and 4.13 %, respectively. The spectral curve correlation and spectral chord angle are 0.998 and 0.979, respectively. The research result can provide technical reference for the processing and application of ZY1 - 02D hyperspectral image surface reflectance products.

Electrostatically Tuned Optical Filters Based on Hybrid Plasmonic-Dielectric Thin Films for Hyperspectral Imaging.

Hyperspectral imaging has a wide range of uses, from medical diagnostics to crop monitoring; however, conventional hyperspectral imaging systems are relatively slow, bulky, and rather costly. In this paper, we present an inexpensive, compact tunable optical filter for hyperspectral applications. The filter is based on a Fabry-Pérot interferometer utilizing hybrid metallic-dielectric mirrors and actuated using a MEMS electrostatic actuator. The optical filter is designed using the transfer matrix method; then, the results were verified by an electromagnetic wave simulator. The actuator is based on a ring-shaped parallel plate capacitor and is designed using COMSOL Multiphysics. An actuation displacement of 170 nm was used, which is the required distance to tune the filter over the whole visible range (400–700 nm). There are two designs proposed for the optical filter: the first was optimized to provide maximum transmission and the other is optimized to have minimum full-width-half-maximum (FWHM) value. The first design has a maximum transmission percentage of 94.45% and a minimum transmission of 86.34%; while the minimum FWHM design had an average FWHM value of 7.267 nm. The results showed improvements over the current commercial filters both in transmission and in bandwidth.

Secondary Iron Mineral Detection via Hyperspectral Unmixing Analysis with Sentinel-2 Imagery

The exposition of minerals to oxygen as well as non-treated tailings in mining activities alter the balance of the ecosystem, specifically leading to the generation of acidic solutions in the presence of sulfidic minerals. Several secondary iron minerals are precipitated in these settings that can be detected via remote sensing applications. The purpose of this research is to investigate the capacity of hyperspectral analysis to determine the abundance of Acid Mine Drainage (AMD)-indicator secondary iron minerals in mine sites with the guidance of ground truth information. To this end, we focus on an abandoned coal mine site in Turkey to detect secondary iron minerals associated with AMD via multispectral Sentinel-2 imagery, in accordance with the laboratory analysis of field-collected samples through X-Ray Diffraction (XRD), Inductive Coupled Plasma (ICP), and ASD spectral analysis. In relation with the conducted laboratory XRD and ICP results, the proposed methodology first reveals the iron-induced absorption feature located between 700 and 900 nm on field-collected ASD spectra and reference USGS spectra through a baseline method, namely parabola fitting method. The subsequent remote sensing analysis then applies hyperspectral unmixing to Sentinel-2 imagery and identifies the spectral endmember indicating iron-absorption behavior by computing its spectral angle distance to reference spectra. The experiments reveal that while the iron absorption characteristics are not apparent in pixel spectra, the utilized unmixing methodology enables capturing of those features at sub-pixel level on the resulting endmembers. Second, the comparison between the calculated abundances with unmixing and iron levels obtained with ground based ICP analysis indicate coherent correlation values. Finally, among the utilized unmixing methods, the performance of SISAL is found better than MVSA with the resulting correlation values of 0.76 and 0.63, respectively, while also returning closer endmembers to the reference iron spectra. The performed research demonstrates the potential of hyperspectral applications on Sentinel-2 data to uncover the sub-pixel iron-induced spectral features in the visible region, proving compatible results between the spectral and laboratory analysis.

Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.

Application of Hyperspectral Imaging in the Assessment of Drought and Salt Stress in Magneto-Primed Triticale Seeds.

Hyperspectral imaging is an appropriate method to thoroughly investigate the microscopic structure of internally heterogeneous agro-food products. By using hyperspectral technology, identifying stress symptoms associated with salinity, before a human observer, is possible, and has obvious benefits. The objective of this paper was to prove the suitability of this technique for the analysis of Triticale seeds subjected to both magneto-priming and drought and salt stress conditions, in terms of image differences obtained among treatments. It is known that, on the one hand, drought and salt stress treatments have negative effects on seeds of almost all species, and on the other hand, magneto-priming enhances seed germination parameters. Thus, this study aimed to relate hyperspectral imaging values—neither positive nor negative in themselves—to the effects mentioned above. Two main conclusions were reached: Firstly, the hyperspectral application is a feasible method for exploring the Triticale structure and for making distinctions under different drought and salt stress treatments, in line with the data variability obtained. Secondly, the lower spectral reflectance in some treatments—in the 400–1000 nm segment—is the result of a great number of chemical compounds in the seed that could be related to magneto-priming.

IDCube Lite: Free Interactive Discovery Cube software for multi- and hyperspectral applications.

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical, machine vision and other fields. The rapidly increasing number of applications requires convenient easy-to-navigate software that can be used by new and experienced users to analyse data, and develop, apply and deploy novel algorithms. Herein, we present our platform, IDCube Lite, an Interactive Discovery Cube that performs essential operations in hyperspectral data analysis to realise the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimise parameters and obtain visual input for the user in a way not previously accessible with other software packages. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new, hidden features that allow users to integrate novel computational methods.

Wide band UV/Vis/NIR blazed-binary reflective gratings: two lithographic techniques investigation

We report on subwavelength reflective gratings for hyperspectral applications operating in the 340 nm-1040 nm spectral range. The blazed grating period is 30 μm and is composed of 2D subwavelength binary structures with sizes in-between 120 nm and 350 nm. We demonstrate the manufacturing of gratings on 3” wafers by two lithography technologies (e-beam or nanoimprint) followed by dry etching process. These subwavelength gratings enable broadband efficiency which is in average 15%-20% above the efficiency requirement for next generation of spectro-imagers for Earth observation missions and a wavefront error that is much smaller than the 100 nm requirement for space application.

FPGA-Based On-Board Hyperspectral Imaging Compression: Benchmarking Performance and Energy Efficiency against GPU Implementations

Remote-sensing platforms, such as Unmanned Aerial Vehicles, are characterized by limited power budget and low-bandwidth downlinks. Therefore, handling hyperspectral data in this context can jeopardize the operational time of the system. FPGAs have been traditionally regarded as the most power-efficient computing platforms. However, there is little experimental evidence to support this claim, which is especially critical since the actual behavior of the solutions based on reconfigurable technology is highly dependent on the type of application. In this work, a highly optimized implementation of an FPGA accelerator of the novel HyperLCA algorithm has been developed and thoughtfully analyzed in terms of performance and power efficiency. In this regard, a modification of the aforementioned lossy compression solution has also been proposed to be efficiently executed into FPGA devices using fixed-point arithmetic. Single and multi-core versions of the reconfigurable computing platforms are compared with three GPU-based implementations of the algorithm on as many NVIDIA computing boards: Jetson Nano, Jetson TX2 and Jetson Xavier NX. Results show that the single-core version of our FPGA-based solution fulfils the real-time requirements of a real-life hyperspectral application using a mid-range Xilinx Zynq-7000 SoC chip (XC7Z020-CLG484). Performance levels of the custom hardware accelerator are above the figures obtained by the Jetson Nano and TX2 boards, and power efficiency is higher for smaller sizes of the image block to be processed. To close the performance gap between our proposal and the Jetson Xavier NX, a multi-core version is proposed. The results demonstrate that a solution based on the use of various instances of the FPGA hardware compressor core achieves similar levels of performance than the state-of-the-art GPU, with better efficiency in terms of processed frames by watt.

A Full-Spectrum Registration Method for Zhuhai-1 Satellite Hyperspectral Imagery.

Accurate registration is an essential prerequisite for analysis and applications involving remote sensing imagery. It is usually difficult to extract enough matching points for inter-band registration in hyperspectral imagery due to the different spectral responses for land features in different image bands. This is especially true for non-adjacent bands. The inconsistency in geometric distortion caused by topographic relief also makes it inappropriate to use a single affine transformation relationship for the geometric transformation of the entire image. Currently, accurate registration between spectral bands of Zhuhai-1 satellite hyperspectral imagery remains challenging. In this paper, a full-spectrum registration method was proposed to address this problem. The method combines the transfer strategy based on the affine transformation relationship between adjacent spectrums with the differential correction from dense Delaunay triangulation. Firstly, the scale-invariant feature transform (SIFT) extraction method was used to extract and match feature points of adjacent bands. The RANdom SAmple Consensus (RANSAC) algorithm and the least square method is then used to eliminate mismatching point pairs to obtain fine matching point pairs. Secondly, a dense Delaunay triangulation was constructed based on fine matching point pairs. The affine transformation relation for non-adjacent bands was established for each triangle using the affine transformation relation transfer strategy. Finally, the affine transformation relation was used to perform differential correction for each triangle. Three Zhuhai-1 satellite hyperspectral images covering different terrains were used as experiment data. The evaluation results showed that the adjacent band registration accuracy ranged from 0.2 to 0.6 pixels. The structural similarity measure and cosine similarity measure between non-adjacent bands were both greater than 0.80. Moreover, the full-spectrum registration accuracy was less than 1 pixel. These registration results can meet the needs of Zhuhai-1 hyperspectral imagery applications in various fields.

Hyperspectral subpixel unmixing via an integrative framework

ABSTRACT In hyperspectral applications, spectral unmixing (SU) is an important technology to obtain the endmembers and the fractional land covers. Spectral variability, outliers, and nonlinearity are three challenging issues, causing SU to extract endmembers and corresponding abundance maps inaccurately. However, in view of the complexity of the three issues, to tackle them at once is difficult and intractable. In this paper, all the aforementioned issues are advocated to process together by a powerful integrative SU framework, where a hyper-manifold learning approach via a sparsity-constrained dual is exploited. In the proposed integrative SU framework, heterogeneous and homogeneous information is explored by dividing the hyperspectral data (HD) into a series of sub-blocks, hyper-Laplacian-based graph is employed to address the nonlinearity, and spectral variability is controlled by a scaled matrix. Moreover, the interferences of the outliers are handled by the augmented correntropy-induced metric (ACIM), where the rare endmembers are separated from the abrupt anomalies via decomposing the HD sets in a low-rank structure and constraining the sparsity on the corresponding residual term. The experimental results on several popularly used real HD sets indicate the superior performances of the proposed approach.

The effects of misregistration between hyperspectral and panchromatic images on linear spectral unmixing

ABSTRACT Low probability subpixel target extraction and identification is important for hyperspectral (HS) applications. To better exploit the signatures of targets and extract targets from mixed pixels, we proposed a linear spectral unmixing algorithm combining HS and panchromatic (Pan) images (called SU-Co-Ims). However, misregistration between HS and Pan images is common, which usually has a negative impact on subsequent applications. Our motivation is to attempt to quantify the misregistration error range in which the extracted subpixel target is valid. To determine the maximum acceptable misregistration error (MAME), we focus on analysing the impact of misregistration between images on target extraction, that is, the effects of misregistration between images on linear spectral unmixing. Taking Pan image as reference, HS image and Pan image are intentionally misregistered in the along-track direction. The proposed SU-Co-Ims method is applied to decompose mixed pixels and extract subpixel targets. Spectral angle mapper (SAM) and Euclidean distance (ED) are used to evaluate spectral unmixing error introduced by misregistration between images. Results indicate that spectral unmixing error increases with misregistration error, and the MAME varies from 0.35 to 1.94 pixels for imagers with different spatial resolution. Consequently, accurate image registration remains crucial to unmixing-based subpixel target extraction, but misregistration has a low impact on results when the misregistration error between images is smaller than the MAME.

REFLECTANCE RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON GAUSSIAN SURFACE FITTING

Abstract. Different from the field of remote sensing, artificial lights are often utilized as the energy source for spectral imaging in the ground hyperspectral applications. The kind of double-spot light source is widely adopted in some large scale ground hyperspectral applications. However, it is hard to reach a satisfied lighting without difference in light intensity in many cases although the lamps are tuned carefully. Therefore, a reflectance calibration of hyperspectral imaging based on the data of diffuse reflectance standard and Gaussian surface fitting is proposed in this paper. The purpose is to improve the reconstruction accuracy of hyperspectral reflectance image by minimized the error caused by the uneven illumination of artificial light source. The method has a higher accuracy than traditional one according to the experiment results.

The Performances of Hyperspectral Sensors for Proximal Sensing of Nitrogen Levels in Wheat.

The accurate and high throughput quantification of nitrogen (N) content in wheat using non-destructive methods is an important step towards identifying wheat lines with high nitrogen use efficiency and informing agronomic management practices. Among various plant phenotyping methods, hyperspectral sensing has shown promise in providing accurate measurements in a fast and non-destructive manner. Past applications have utilised non-imaging instruments, such as spectrometers, while more recent approaches have expanded to hyperspectral cameras operating in different wavelength ranges and at various spectral resolutions. However, despite the success of previous hyperspectral applications, some important research questions regarding hyperspectral sensors with different wavelength centres and bandwidths remain unanswered, limiting wide application of this technology. This study evaluated the capability of hyperspectral imaging and non-imaging sensors to estimate N content in wheat leaves by comparing three hyperspectral cameras and a non-imaging spectrometer. This study answered the following questions: (1) How do hyperspectral sensors with different system setups perform when conducting proximal sensing of N in wheat leaves and what aspects have to be considered for optimal results? (2) What types of photonic detectors are most sensitive to N in wheat leaves? (3) How do the spectral resolutions of different instruments affect N measurement in wheat leaves? (4) What are the key-wavelengths with the highest correlation to N in wheat? Our study demonstrated that hyperspectral imaging systems with satisfactory system setups can be used to conduct proximal sensing of N content in wheat with sufficient accuracy. The proposed approach could reduce the need for chemical analysis of leaf tissue and lead to high-throughput estimation of N in wheat. The methodologies here could also be validated on other plants with different characteristics. The results can provide a reference for users wishing to measure N content at either plant- or leaf-scales using hyperspectral sensors.

Nonlinear Anomaly Detection Based on Spectral–Spatial Composite Kernel for Hyperspectral Images

Anomaly detection is an important task among hyperspectral applications, which detects the anomalous targets that are spectrally different from their surroundings. Most of the anomaly detectors construct the detection models in the linear space, where the nonlinear characteristics of data are not taken into consideration. The kernel-based methods, which implicitly map the data into a high-dimensional feature space, have shown great potential in dealing with nonlinear problems. Nevertheless, the original kernel-based detection methods only exploit the spectral features without taking advantage of the spatial correlations among adjacent pixels. In this letter, a nonlinear spectral-spatial composite kernel-based detector (SSCKD) is proposed for hyperspectral anomaly detection. First, superpixel segmentation is adopted to extract spatial features according to local homogeneity. Then, considering the nonlinear characteristics in the hyperspectral data, spectral and spatial features are combined by the composite kernel. Finally, an iterative composite kernel-learning procedure based on the centered kernel alignment is designed to adaptively determine the kernel weights. The experiments are conducted on three real hyperspectral data sets. The detection results demonstrate the superiority of the proposed method in comparison with the conventional and state-of-the-art anomaly detection methods.

Hyperspectral Unmixing Using Orthogonal Sparse Prior-Based Autoencoder With Hyper-Laplacian Loss and Data-Driven Outlier Detection

Hyperspectral unmixing, which estimates end-members and their corresponding abundance fractions simultaneously, is an important task for hyperspectral applications. In this article, we propose a new autoencoder-based hyperspectral unmixing model with three novel components. First, we propose a new sparse prior to abundance maps. The proposed prior, called orthogonal sparse prior (OSP), is based on the observations that different abundance maps are close to orthogonal because, generally, no more than two end-members are mixed within one pixel. As opposed to the conventional norm-based sparse prior that assumes the abundance maps are independent, the proposed OSP explores the orthogonality between the abundance maps. Second, we propose the hyper-Laplacian loss to model the reconstruction error. The key observation is that the reconstruction error distribution usually has a heavy-tailed shape, which is better modeled by the hyper-Laplacian distribution rather than the commonly used Gaussian distribution. Third, to ease the side effect of outliers for end-member initializations, we develop a data-driven approach to detect outliers from the raw hyperspectral images. Extensive experiments on both synthetic and real-world data sets show that the proposed method significantly and consistently outperforms the compared state-of-the-art methods, with up to more than 50% improvements.

Обзор зарубежных достижений за последние пять лет в области использования мульти- и гиперспектральных спутниковых данных и современных методов их обработки в геологических исследованиях

Обзор зарубежных достижений за последние пять лет в области использования мульти- и гиперспектральных спутниковых данных и современных методов их обработки в геологических исследованиях

Learning Deep Hierarchical Spatial-Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN.

Every pixel in a hyperspectral image contains detailed spectral information in hundreds of narrow bands captured by hyperspectral sensors. Pixel-wise classification of a hyperspectral image is the cornerstone of various hyperspectral applications. Nowadays, deep learning models represented by the convolutional neural network (CNN) provides an ideal solution for feature extraction, and has made remarkable achievements in supervised hyperspectral classification. However, hyperspectral image annotation is time-consuming and laborious, and available training data is usually limited. Due to the “small-sample problem”, CNN-based hyperspectral classification is still challenging. Focused on the limited sample-based hyperspectral classification, we designed an 11-layer CNN model called R-HybridSN (Residual-HybridSN) from the perspective of network optimization. With an organic combination of 3D-2D-CNN, residual learning, and depth-separable convolutions, R-HybridSN can better learn deep hierarchical spatial–spectral features with very few training data. The performance of R-HybridSN is evaluated over three public available hyperspectral datasets on different amounts of training samples. Using only 5%, 1%, and 1% labeled data for training in Indian Pines, Salinas, and University of Pavia, respectively, the classification accuracy of R-HybridSN is 96.46%, 98.25%, 96.59%, respectively, which is far better than the contrast models.

Graph and Total Variation Regularized Low-Rank Representation for Hyperspectral Anomaly Detection

Anomaly detection is of great importance among hyperspectral applications, which aims at locating targets that are spectrally different from their surrounding background. A variety of anomaly detection methods have been proposed in the past. However, most of them fail to take the high spectral correlations of all the pixels into consideration. Low-rank representation (LRR) has drawn a great deal of interest in recent years, as a promising model to exploit the intrinsic low-rank property of hyperspectral images. Nevertheless, the original LRR model only analyzes the spectral signatures without taking advantage of the valuable spatial information in hyperspectral images. Furthermore, it has been shown that the local geometrical information of the hyperspectral data is also important for discrimination between the anomalies and background pixels. In this article, we incorporate the graph regularization and total variation (TV) regularization into the LRR formulation and propose a novel anomaly detection method based on graph and TV regularized LRR (GTVLRR) model, to preserve the local geometrical structure and spatial relationships in hyperspectral images. Extensive experiments have been conducted on both simulated and real hyperspectral data sets. The experimental results demonstrate the superiority of the proposed method over conventional and state-of-the-art anomaly detection methods.