Hyperspectral Information Research Articles

Estimating Chlorophyll Fluorescence Parameters of Rice (Oryza sativa L.) Based on Spectrum Transformation and a Joint Feature Extraction Algorithm

The chlorophyll fluorescence parameter Fv/Fm plays a significant role in indicating the photosynthetic function of plants. The existing technical methods used to measure Fv/Fm are often inefficient and cumbersome. To realize fast and non-destructive monitoring of Fv/Fm, this study took rice under different fertilizer treatments and measured the hyperspectral reflectance information and Fv/Fm data of rice leaves during the whole growth period. Five spectral transformation methods were used to pre-process the spectral data. Then, spectral characteristic wavelengths were extracted by the correlation coefficient method (CC) combined with the competitive adaptative reweighted sampling (CARS) algorithm. Finally, based on the combination of characteristic wavelengths extracted from different spectral transformations, back propagation neural network (BPNN) models were constructed and evaluated. The results showed that: (1) first derivative transform (FD), multiplicative scatter correction (MSC) and standardized normal variation (SNV) methods could effectively highlight the correlation between spectral data and Fv/Fm. The most sensitive bands with high correlation coefficients were concentrated in the range of 650–850 nm, and the absolute values of the highest correlation coefficients were 0.84, 0.73, and 0.72, respectively. (2) The CC-CARS algorithm could effectively screen the characteristic wavelengths sensitive to Fv/Fm. The number of sensitive bands extracted by FD, MSC, and SNV pre-treatment methods were 14, 13, and 16 which only accounted for 2.33%, 2.16%, and 2.66% of the total spectral wavelength (the number of full spectral bands is 601), respectively. (3) The BPNN models were established based on the above sensitive wavelengths, and it was found that MSC-CC-CARS-BPNN had the highest prediction accuracy, and its testing set R2, RMSE and RPD were 0.74, 1.88% and 2.46, respectively. The results can provide technical references for hyperspectral data pre-processing and rapid and non-destructive monitoring of chlorophyll fluorescence parameters.

SI2FM: SID Isolation Double Forest Model for Hyperspectral Anomaly Detection

Hyperspectral image (HSI) anomaly detection (HSI-AD) has become a hot issue in hyperspectral information processing as a method for detecting undesired targets without a priori information against unknown background and target information, which can be better adapted to the needs of practical applications. However, the demanding detection environment with no prior and small targets, as well as the large data and high redundancy of HSI itself, make the study of HSI-AD very challenging. First, we propose an HSI-AD method based on the nonsubsampled shearlet transform (NSST) domain spectral information divergence isolation double forest (SI2FM) in this paper. Further, the method excavates the intrinsic deep correlation properties between NSST subband coefficients of HSI in two ways to provide synergistic constraints and guidance on the prediction of abnormal target coefficients. On the one hand, with the “difference band” as a guide, the global isolation forest and local isolation forest models are constructed based on the spectral information divergence (SID) attribute values of the difference band and the low-frequency and high-frequency subbands, and the anomaly scores are determined by evaluating the path lengths of the isolation binary tree nodes in the forest model to obtain a progressively optimized anomaly detection map. On the other hand, based on the relationship of NSST high-frequency subband coefficients of spatial-spectral dimensions, the three-dimensional forest structure is constructed to realize the co-optimization of multiple anomaly detection maps obtained from the isolation forest. Finally, the guidance of the difference band suppresses the background noise and anomaly interference to a certain extent, enhancing the separability of target and background. The two-branch collaborative optimization based on the NSST subband coefficient correlation mining of HSI enables the prediction of anomaly sample coefficients to be gradually improved from multiple perspectives, which effectively improves the accuracy of anomaly detection. The effectiveness of the algorithm is verified by comparing real hyperspectral datasets captured in four different scenes with eleven typical anomaly detection algorithms currently available.

Spectral replacement using machine learning methods for continuous mapping of the Geostationary Environment Monitoring Spectrometer (GEMS)

Abstract. Earth radiances in the form of hyperspectral measurements contain useful information on atmospheric constituents and aerosol properties. The Geostationary Environment Monitoring Spectrometer (GEMS) is an environmental sensor measuring such hyperspectral data in the ultraviolet and visible spectral range over the Asia–Pacific region. After completion of the in-orbit test of GEMS in October 2020, bad pixels are found as one of remaining calibration issues resulting in obvious spatial gaps in the measured radiances as well as retrieved properties. To solve the fundamental cause of the issue, this study takes an approach reproducing the defective spectra with machine learning models using artificial neural network (ANN) and multivariate linear regression (Linear). Here the models are trained with defect-free measurements of GEMS after dimensionality reduction with principal component analysis (PCA). Results show that the PCA-Linear model has small reproduction errors for a narrower spectral gap and is less vulnerable to outliers with an error of 0.5 %–5 %. On the other hand, the PCA-ANN model shows better results emulating strong non-linear relations with an error of about 5 % except for the shorter wavelengths around 300 nm. It is demonstrated that dominant spectral patterns can be successfully reproduced with the models within the level of radiometric calibration accuracy of GEMS, but a limitation remains when it comes to finer spectral features. When applying the reproduced spectra to retrieval processes of cloud and ozone, cloud centroid pressure shows an error of around 1 %, while total ozone column density shows relatively higher variance. As an initial step reproducing spectral patterns for bad pixels, the current study provides the potential and limitations of machine learning methods to improve hyperspectral measurements from the geostationary orbit.

Hyperspectral Image Classification Based on the Gabor Feature with Correlation Information

Gabor filter is widely used to extract spatial texture features of hyperspectral images (HSI) for HSI classification; however, a single Gabor filter cannot obtain the complete image features. In the paper, we propose an HSI classification method that combines the Gabor filter (GF) and domain-transformation standard convolution (DTNC) filter. First, we use the Gabor filter to extract spatial texture features from the first two principal components of the dimensionality-reduction HSI with PCA. Second, we use the DTNC filter to extract spatial correlation features from HSI in all bands. Finally, the Large Margin Distribution Machine (LDM) uses the linear fusion of the two kinds of spatial features to classify HSI. The experimental results show that the classification accuracy of Indian Pines, Pavia University, and Kennedy Space Center data sets is 96.64, 98.23, and 98.95% with only 4, 3, and 6% training samples, respectively; and these accuracies are 2–20% higher than the other tested methods. Compared with the hyperspectral information based on SVM, EPF, IFRF, PCA-EPFs, LDM-FL, and GFDN method, the proposed method, GFDTNCLDM, significantly improves the accuracy of HSI classification.

Spectral Super-Resolution Based on Dictionary Optimization Learning via Spectral Library

Extensive works have been reported in hyperspectral images (HSIs) and multispectral images (MSIs) fusion to raise the spatial resolution of HSIs. However, the limited acquisition of HSIs has been an obstacle to such approaches. Spectral super-resolution (SSR) of MSI is a challenging and less investigated topic, which can also provide high-resolution synthetic HSIs. To deal with this high ill-posedness problem, we perform super-resolution enhancement of MSIs in the spectral domain by incorporating a spectral library as a priori. First, an aligned spectral library, which maps the open-source spectral library to a specific spectral library created for the reconstructed HR HSI, is represented. An intermediate latent HSI is obtained by fusing the spatial information from MSI and the hyperspectral information from a specific spectral library. Then, we use low-rank attribute embedding to transfer latent HSI into a robust subspace. Finally, a low-rank HSI dictionary representing the hyperspectral information is learned from the latent HSI. The adaptive sparse coefficient of MSI is obtained with a nonnegative constraint. By fusing these two terms, we get the final HR HSI. The proposed SSR model does not require any pretraining stages. We confirm the validity and superiority of our proposed SSR algorithm by comparing it with several benchmark state-of-the-art approaches on different datasets.

Hyperspectral absorption of CO in the near infrared band at room temperature

As an important medial product in the combustion process of carbon-based compounds, CO serves as one of the preferable candidates for combustion diagnosis in absorption spectrum. So far, most of researches have focused on the conventional one-line or dual-line technique, which requires a number of beam projections for two-dimension (2D) tomography of combustion field. Hyperspectral absorption spectroscopy enables continuous acquisition of absorption information over a whole absorption band, rather than one or two discrete absorption lines, demonstrating remarkable advantage over the traditional one-line and dual-line techniques. Hyperspectral absorption spectroscopy can not only reduce the system complexity with limited projections for high spatial resolution 2D tomography, but also improve the system applicability by refining the measurement robustness. However, up to now, little attention has been paid to hyperspectral absorption of CO. Here, by using a wideband tunable laser source around 1.5 μm, experiments are conducted at room temperature to investigate the hyperspectral absorption characteristics of CO in the near infrared band. Absorptions under different pressure conditions are compared with each other. And, the measured results are consistent with the HITRAN2016 database. With the measured hyperspectral absorption information in the 1565–1570 nm range, temperature and mole fraction of CO are derived by the first derivative method. Despite the distortion of the recorded absorption peaks, accurate results are obtained with measurement errors within 5% for both temperature and mole fraction, thereby validating the reliability of hyperspectral absorption technique for CO. And, this research is instructive for future 2D tomography of combustion fields based on hyperspectral absorption of CO.

Hyperspectral Image Denoising via Robust Subspace Estimation and Group Sparsity Constraint

Hyperspectral cameras capture electromagnetic information within hundreds of narrow spectral bands, producing hyperspectral images (HSIs) with the capability to accurately characterize the attribute information of objects. However, mixed noise induced by instrument and atmospheric effects hinders the interpretations and applications of the HSIs. In this paper, we propose a novel subspace representation based mixed noise removal method for hyperspectral images via Robust Subspace Estimation and weighted Group Sparsity constraint (RoSEGS). An outlier detection method is proposed to effectively detect sparse noise and replace the sparse noise with new estimates. A subspace estimation strategy, which is robust to mixed noise, is proposed. The subspace is first estimated after sparse noise detection and then optimized iteratively. In addition to the introduction of a state-of-the-art denoiser based on the plug-and-play technique to exploit self-similarity characteristics of the eigen-images, we impose a weighted group sparse regularization on the eigen-images to better promote the group sparsity of the spatial differences between the eigen-images, which further improves the denoising performance. We performed extensive experiments on two simulated and two real HSIs to fully demonstrate the effectiveness of the proposed method in comparison with seven state-of-the-art competitors.

HyperQUEEN: Hyperspectral Quantum Deep Network For Image Restoration

Quantum science just winning the 2022 Nobel Prize in Physics must lead future development of remote sensing technologies. However, given the very limited number of entangled quantum bits (qubits) even in the most advanced quantum computers, is processing remotely sensed hyperspectral image (featured by its large data volume) using quantum computer technically feasible? Even if the quantum image state can be well processed to the quantum state of the target image (QSTI), it cannot be perfectly retrieved/output as the QSTI will collapse to some eigenstate once it is measured. Owing to these challenges, current quantum image processing technologies can only achieve classification-level applications requiring just a few output qubits. We design a hyperspectral quantum deep network (HyperQUEEN) to encode the hyperspectral information using very few qubits, as well as to learn the mapping from some measuring statistics (associated with the collapsed-QSTI) to the target image (instead of directly retrieving the unobservable QSTI), thereby solving the challenges. HyperQUEEN is the first quantum architecture that makes a breakthrough to blindly reconstruct NASA’s damaged hyperspectral images, which means a lot for the upcoming space era. As the immature quantum facility nowadays does not yet allow us to fully exhibit its high potential, we are not aiming at developing state-of-the-art methods, but are demonstrating the feasibility of quantum hyperspectral remote sensing. Mathematical analysis guiding our design toward the low-rank quantum deep network, together with comprehensive experiments, will also be reported.

A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

Soil Water Content Regression Analysis of Measurement Data from Hyperspectral Camera in Weathered Granite Soils

Soil water content is one of the most common physical parameters that cause landslides or debris flow. Therefore, it is of very importance to determine or predict the water content quickly and non-destructively. This study investigates the hyperspectral information in the visible near-infrared regions (VNIR) of different samples of granite soils possessing varying water content. Totally 162 granite samples were taken from each mountain area. The samples with different water contents were examined using a hyperspectral radiometer operating in the 400~1000nm range by obtaining the spectral curves. It was found that the variation of VNIR is consistent with the water content variation of weathered granite soils, and the hyperspectral camera was able to detect it. A Partial Least Squares Regression (PLSR) analysis was applied to develop a calibration-model. The PLSR model resulted in a good correlation between the water content and VNIR profile. The results demonstrate that the hyperspectral camera combined with the PLSR model can be a useful and non-destructive tool for determining soil water contents in the weathered granite soils.

Comparison of Classic Classifiers, Metaheuristic Algorithms and Convolutional Neural Networks in Hyperspectral Classification of Nitrogen Treatment in Tomato Leaves

Tomato is an agricultural product of great economic importance because it is one of the most consumed vegetables in the world. The most crucial chemical element for the growth and development of tomato is nitrogen (N). However, incorrect nitrogen usage can alter the quality of tomato fruit, rendering it undesirable to customers. Therefore, the goal of the current study is to investigate the early detection of excess nitrogen application in the leaves of the Royal tomato variety using a non-destructive hyperspectral imaging system. Hyperspectral information in the leaf images at different wavelengths of 400–1100 nm was studied; they were taken from different treatments with normal nitrogen application (A), and at the first (B), second (C) and third (D) day after the application of excess nitrogen. We investigated the performance of nine machine learning classifiers, including two classic supervised classifiers, i.e., linear discriminant analysis (LDA) and support vector machines (SVMs), three hybrid artificial neural network classifiers, namely, hybrid artificial neural networks and independent component analysis (ANN-ICA), harmony search (ANN-HS) and bees algorithm (ANN-BA) and four classifiers based on deep learning algorithms by convolutional neural networks (CNNs). The results showed that the best classifier was a CNN method, with a correct classification rate (CCR) of 91.6%, compared with an average of 85.5%, 68.5%, 90.8%, 88.8% and 89.2% for LDA, SVM, ANN-ICA, ANN-HS and ANN-BA, respectively. This shows that modern CNN methods should be preferred for spectral analysis over other classical techniques. These CNN architectures can be used in remote sensing for the precise detection of the excessive use of nitrogen fertilizers in large extensions.

Extraction of mineralized indicator minerals using ensemble learning model optimized by SSA based on hyperspectral image

Abstract Mineralized indicator minerals are an important geological and mineral exploration indicator. Rapid extraction of mineralized indicator minerals from hyperspectral remote sensing images using ensemble learning model has important geological significance for mineral resources exploration. In this study, two mineralized indicator minerals, limonite and chlorite, exposed at the surface of Qinghai Gouli area were used as the research objects. Sparrow search algorithm (SSA) was combined with random forest (RF) and gradient boosting decision tree (GBDT) ensemble learning models, respectively, to construct hyperspectral mineralized indicative mineral information extraction models in the study area. Youden index (YD) and ore deposit coincidence (ODC) were applied to evaluate the performance of different models in the mineral information extraction. The results indicate that the optimization of SSA parameter algorithm is obvious, and the accuracy of both the integrated learning models after parameter search has been improved substantially, among which the SSA-GBDT model has the best performance, and the YD and the ODC can reach 0.661 and 0.727, respectively. Compared with traditional machine learning model, integrated learning model has higher reliability and stronger generalization performance in hyperspectral mineral information extraction and application, with YD greater than 0.6. In addition, the distribution of mineralized indicative minerals extracted by the ensemble learning model after parameter optimization is basically consistent with the distribution pattern of the fracture tectonic spreading characteristics and known deposits (points) in the area, which is in line with the geological characteristics of mineralization in the study area. Therefore, the classification and extraction model of minerals based on hyperspectral remote sensing technology, combined with the SSA optimization algorithm and ensemble learning model, is an efficient mineral exploration method.

Anti-spoofing face recognition using a metasurface-based snapshot hyperspectral image sensor

Modern face recognition systems usually combine RGB, depth, and infrared cameras to do face antispoofing, but they are still not robust enough to unknown 3D high-quality mask attack. In our work, we developed a snapshot hyperspectral image sensor based on metasurface nanostructures to obtain the high-precision hyperspectral information of the detected face, and we built a practical antispoofing face recognition system using our new sensor. Experiments show that our sensor can reconstruct the reflectance spectrum of human skin, and this spectral information captured by our sensor can be quite effective and robust to identify spoof faces. We attack our system with several types of spoof faces, and our system reaches 97.98% accuracy in real-world testing scenes.

Spectrum Index for Estimating Ground Water Content Using Hyperspectral Information

Quality control considerably affects road stability and operability and is directly linked to the underlying ground compaction. The degree of compaction is largely determined by water content, which is typically measured at the actual construction site. However, conventional methods for measuring water content do not capture entire construction sites efficiently. Therefore, this study aimed to apply remote sensing of hyperspectral information to efficiently measure the groundwater content of large areas. A water content prediction equation was developed through an indoor experiment. The experimental samples comprised 0–40% (10% increase) of fine contents added to standard sand. As high water content is not required in road construction, 0–15% (1% increase) of water content was added. The test results were normalized, the internal and external environments were controlled for precise results, and a wavelength–reflection curve was derived for each test case. Data variability analyses were performed, and the appropriate wavelength for water content reflection, as well as reflectance, was determined and converted into a spectrum index. Finally, various fitting models were applied to the corresponding spectrum index for water content prediction. Reliable results were obtained with the reflectance corresponding to a wavelength of 720 nm applied as the spectrum index.

Temporal instability of partial least squares regressions for estimating leaf photosynthetic traits from hyperspectral information

Partial least squares regression (PLSR) is applied increasingly often to predict plant photosynthesis from reflectance spectra. While its applicability across different areas has been examined in previous studies, its stability across time has yet to be evaluated. In this study, we assessed a series of PLSR models built upon three different band selection approaches (iterative stepwise, genetic algorithm, and uninformative variable elimination), in combination with different spectral transforms (original and first-order derivative spectra), for their stabilities in predicting the maximum carboxylation rate (Vcmax) and maximum electron transport rate (Jmax) from hyperspectral reflectance spectra at different temporal scales (seasonal and interannual). The results showed that both photosynthetic parameters can be estimated from leaf hyperspectral reflectance with moderate to good accuracy across different growing stages (R2 = 0.45–0.84) and years (R2 = 0.37–0.97). We further found that the iterative stepwise selection of informative bands when building PLSR models could greatly improve its predictive capacity compared with that of other PLSR models, especially those based on first-order derivative spectra. However, the selected bands of the models for both photosynthetic parameters were, unfortunately not consistent. Furthermore, we could not have identified any model with fixed spectra performed consistently across different seasonal stages and across different years. However, the blue spectral regions were popularly selected throughout the growing stages and in different years. The results demonstrate that leaf spectra-trait estimation using PLSR models varies with time and thus cast doubt over the use of a specific PLSR model to infer leaf traits across different temporal-spatial contexts. The development of a general applicable PLSR model is still in the works.

Quantitative study on impact damage of yellow peach based on hyperspectral image information combined with spectral information

Yellow peaches are graded based on their degree of impact damage to reduce economic losses, and the values of mechanical parameters can be used to characterize the degree of impact damage of them. In this paper, the combined hyperspectral image features and spectral features method was proposed to predict the values of mechanical parameters of yellow peaches. A collision device based on the pendulum principle was built to acquire the mechanical parameters which were maximum force, absorbed energy and average pressure. A hyperspectral imaging system was used to acquire the images of the bruised yellow peaches and the spectral information and color characteristics of images were extracted, respectively. The damage areas of yellow peaches were obtained based on the combined principal component analysis (PCA) and threshold segmentation method. Texture features of the characteristic wavelength images were extracted based on the grey-level co-generation matrix (GLCM). The PLSR models of mechanical parameters were built based on spectral information, image information, and spectral information combined with image information, respectively. The results show that the PLSR models based on spectral information combined with image information are best in prediction the values of damage area and maximum force. However, the PLSR models have not achieved satisfactory performance in prediction the values of absorbed energy and average pressure. To improve the performance of PLSR models of absorbed energy and average pressure, the feature wavelengths were selected by CARS, and the correlation analysis was used between image information and mechanical parameters. The PLSR models were built based on feature spectral information, image feature information, and feature spectral information combined with image feature information, respectively. The results show that the accuracy of all mechanical parameters is significantly improved by the PLSR models based on the feature spectral information combined with the image feature information. The prediction set correlation coefficient (RP) and the root mean square error (RMSEP) of the damaged area, absorbed energy, maximum force and average pressure are 0.928 and 86.632 mm2, 0.826 and 1.469 J, 0.924 and 61.765 N, 0.815 and 0.050 MPa, respectively. The results of the study not only provide a theoretical basis for postharvest grading of fruit, but also provide a reference for analyzing the mechanical properties of fruit.

Prediction of Ground Water Content Using Hyperspectral Information through Laboratory Test

With the technological advances led by the fourth industrial revolution, automation has been implemented in road earthworks and paving in the road construction sector. For preparation of construction works, achieving an optimal degree of compaction of the subgrade soil is one of the key factors required for automation of construction and digitalization of quality control. The degree of compaction is greatly affected by water content in geotechnical aspects, and measurement of water content is a necessary process in construction sites. However, conventional methods of water content measurement have limitations and drawbacks and have low efficiency considering the recent trend of construction automation and digitalization of quality control. Therefore, in this study, hyperspectral remote sensing was applied for efficient large-scale measurement of water content over a wide area. To this end, first, through laboratory tests, soil water content was normalized with spectral information. A spectrum was derived with a varying water content using standard sand, and reflectance was obtained for specific ranges of wavelength. Finally, we obtained the relationship between the reflectance and the water content by comparing with various fitting models. At this time, the ranges of wavelength to be used in the equation were specified and presented in an exponential model.

Spectral-spatial classification of hyperspectral images based on non-linear principal component analysis and deep learning models

ABSTRACT Hyperspectral sensors, due to the acquisition of a large number of spectral bands, always have particular importance in monitoring the phenomena of the earth’s surface. The classification of hyperspectral images is the most crucial method of processing hyperspectral data, and many efforts have been made to increase its accuracy. In recent years, convolutional neural networks (CNNs) and spatial features have been a big part of how well hyperspectral images can be classified. CNNs, due to the automatic generation of features and reduction of parameters, compared to multilayer perceptron neural networks by sharing parameters in each layer, have received much attention from researchers in the field of pattern recognition. In previous research, much attention has not been paid to the simultaneous use of spatial feature extraction methods in CNNs. For this reason, in this paper, a new CNNs architecture has been introduced to classify hyperspectral images, which uses the spectral-spatial vector obtained from the morphological profiles method as the network’s input. Morphological profiles, which include a set of opening and closing filters, are generally applied to the components of the image that contain the most information. In this research, non-linear principal component analysis (non-linear PCA) implemented by neural networks was used to summarize hyperspectral image information. Therefore, the main innovation of this paper is to provide a three-stage framework for applying deep learning. The first step is to reduce the dimensions of the hyperspectral image using non-linear PCA, the second stage is to extract spatial features using the morphological profiles method, and the third step is to prepare the inputs and design the CNNs architecture. The proposed method was evaluated on three benchmark hyperspectral images of Pavia, Berlin and Telops. The results of the experiments show the superiority of the proposed method by 13, 11 and 17% in the overall accuracy parameter compared to the support vector machines (SVM) classification algorithm in the Pavia, Berlin and Telops images, respectively.

Air Pollution Detection Using a Novel Snap-Shot Hyperspectral Imaging Technique.

Air pollution has emerged as a global problem in recent years. Particularly, particulate matter (PM2.5) with a diameter of less than 2.5 μm can move through the air and transfer dangerous compounds to the lungs through human breathing, thereby creating major health issues. This research proposes a large-scale, low-cost solution for detecting air pollution by combining hyperspectral imaging (HSI) technology and deep learning techniques. By modeling the visible-light HSI technology of the aerial camera, the image acquired by the drone camera is endowed with hyperspectral information. Two methods are used for the classification of the images. That is, 3D Convolutional Neural Network Auto Encoder and principal components analysis (PCA) are paired with VGG-16 (Visual Geometry Group) to find the optical properties of air pollution. The images are classified into good, moderate, and severe based on the concentration of PM2.5 particles in the images. The results suggest that the PCA + VGG-16 has the highest average classification accuracy of 85.93%.

Hyperspectral reconstruction from a single textile RGB image based on the generative adversarial network

Hyperspectral images are capable of significantly increasing the accuracy of textile color measurement because of their rich information. However, hyperspectral imaging generally requires expensive equipment and complex operations. If the hyperspectral information can be reconstructed based on a single RGB image, it can facilitate the widespread application of hyperspectral imaging technology, such as in textile color measurement. In this paper, a deep learning model was proposed for hyperspectral reconstruction of cotton and linen fabrics based on the conditional generative adversarial network. According to this model, the encoder–decoder structure and spatial pyramid convolution pooling operation were adopted to fuse multi-scale features for the prevention of mode collapse. Atrous convolution was introduced to increase the receptive field to adapt to the fabric texture information, and the hyperspectral information of the fabric from a single RGB image was reconstructed. The quantitative and qualitative tests verified that the method in this paper had good results. The root mean square error and peak signal-to-noise ratio were 0.0271 and 31.372, respectively, for reconstructed fabric hyperspectral images; the highest average color difference [Formula: see text] in the reconstructed hyperspectral colorimetry experiment was obtained as 2.755. Thus, the proposed method can meet the common application requirements of color measurement.