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Hyperspectral and thermal temperature estimation during laser cladding
摘要： Although there is no doubt about the tremendous industrial potential of metal additive manufacturing techniques such as laser metal deposition, the technology still has some intrinsic quality challenges to overcome before reaching its industrial maturity. Noncontact in situ monitoring of the temperature evolution of the workpiece could provide the necessary information to implement an automated closed-loop process control system and optimize the manufacturing process, providing a robust solution to these issues. However, measuring absolute temperatures is not self-evident: wavelength-dependent emissivity values vary between solid, liquid, and mushy metallic regions, requiring spectral information and dedicated postprocessing to relate the amount of emitted infrared radiation to the material temperature. This paper compares the temperature estimation results obtained from a visible and near-infrared hyperspectral line camera and a conventional short-wave infrared (SWIR) thermal camera during the laser melting and cladding of a 316L steel sample. Both methods show agreeing results for the temperature distribution inside the melt pool, with the SWIR camera extending the temperature measurements beyond the melt pool boundaries into the solid region.
关键词： temperature estimation,laser cladding,hyperspectral imaging,additive manufacturing,thermal monitoring
更新于2025-11-28 14:24:20
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Identifying Mangrove Species Using Field Close-Range Snapshot Hyperspectral Imaging and Machine-Learning Techniques
摘要： Investigating mangrove species composition is a basic and important topic in wetland management and conservation. This study aims to explore the potential of close-range hyperspectral imaging with a snapshot hyperspectral sensor for identifying mangrove species under field conditions. Specifically, we assessed the data pre-processing and transformation, waveband selection and machine-learning techniques to develop an optimal classification scheme for eight mangrove species in Qi’ao Island of Zhuhai, Guangdong, China. After data pre-processing and transformation, five spectral datasets, which included the reflectance spectra R and its first-order derivative d(R), the logarithm of the reflectance spectra log(R) and its first-order derivative d[log(R)], and hyperspectral vegetation indices (VIs), were used as the input data for each classifier. Consequently, three waveband selection methods, including the stepwise discriminant analysis (SDA), correlation-based feature selection (CFS), and successive projections algorithm (SPA) were used to reduce dimensionality and select the effective wavebands for identifying mangrove species. Furthermore, we evaluated the performance of mangrove species classification using four classifiers, including linear discriminant analysis (LDA), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM). Application of the four considered classifiers on the reflectance spectra of all wavebands yielded overall classification accuracies of the eight mangrove species higher than 80%, with SVM having the highest accuracy of 93.54% (Kappa = 0.9256). Using the selected wavebands derived from SPA, the accuracy of SVM reached 93.13% (Kappa = 0.9208). The addition of hyperspectral VIs and d[log(R)] spectral datasets further improves the accuracies to 93.54% (Kappa = 0.9253) and 96.46% (Kappa = 0.9591), respectively. These results suggest that it is highly effective to apply field close-range snapshot hyperspectral images and machine-learning classifiers to classify mangrove species.
关键词： machine learning,waveband selection,mangrove species classification,close-range hyperspectral imaging,field hyperspectral measurement
更新于2025-09-23 15:23:52
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A Runtime-Scalable and Hardware-Accelerated Approach to On-Board Linear Unmixing of Hyperspectral Images
摘要： Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo3 framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo3-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth.
关键词： FPGAs,hyperspectral imaging,on-board processing,ARTICo3,linear unmixing
更新于2025-09-23 15:23:52
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Use of Hyperspectral Image Data Outperforms Vegetation Indices in Prediction of Maize Yield
摘要： Hyperspectral cameras can provide reflectance data at hundreds of wavelengths. This information can be used to derive vegetation indices (VIs) that are correlated with agronomic and physiological traits. However, the data generated by hyperspectral cameras are richer than what can be summarized in a VI. Therefore, in this study, we examined whether prediction equations using hyperspectral image data can lead to better predictive performance for grain yield than what can be achieved using VIs. For hyperspectral prediction equations, we considered three estimation methods: ordinary least squares, partial least squares (a dimension reduction method), and a Bayesian shrinkage and variable selection procedure. We also examined the benefits of combining reflectance data collected at different time points. Data were generated by CIMMYT in 11 maize (Zea mays L.) yield trials conducted in 2014 under heat and drought stress. Our results indicate that using data from 62 bands leads to higher prediction accuracy than what can be achieved using individual VIs. Overall, the shrinkage and variable selection method was the best-performing one. Among the models using data from a single time point, the one using reflectance collected at 28 d after flowering gave the highest prediction accuracy. Combining image data collected at multiple time points led to an increase in prediction accuracy compared with using single-time-point data.
关键词： maize yield,hyperspectral imaging,prediction accuracy,vegetation indices,Bayesian methods
更新于2025-09-23 15:23:52
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A deep learning based feature extraction method on hyperspectral images for nondestructive prediction of TVB-N content in Pacific white shrimp (Litopenaeus vannamei)
摘要： Hyperspectral imaging (HSI) technique with spectral range of 900e1700 nm was implemented to predict total volatile basic nitrogen (TVB-N) content in Pacific white shrimp. Successive projections algorithm (SPA) and deep-learning-based stacked auto-encoders (SAEs) algorithm were comparatively used for spectral feature extraction. Least-squares support vector machine (LS-SVM), partial least squares regression (PLSR) and multiple linear regression (MLR) were used for prediction. The results demonstrated that the SAEs-based prediction models (SAEs-LS-SVM, SAEs-MLR and SAEs-PLSR) performed better than either full wavelengths-based or SPA-based prediction models. The SAEs-LS-SVM was considered to be the best model with RP2 value of 0.921, RMSEP value of 6.22 mg N [100 g]?1, RPD value of 3.58 and computational time of 3.9 ms for predicting TVB-N in prediction set. The results of this study indicated that SAEs has a high potential in the multivariate analysis of hyperspectral images for shrimp quality inspections.
关键词： Stacked auto-encoders,Pacific white shrimp,Total volatile basic nitrogen,Nondestructive prediction,Hyperspectral imaging
更新于2025-09-23 15:23:52
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Near-Infrared Hyperspectral Imaging Rapidly Detects the Decay of Postharvest Strawberry Based on Water-Soluble Sugar Analysis
摘要： This paper presents a novel strategy to detect the fungal decay in strawberry using reflectance near-infrared hyperspectral imaging (NIR-HSI, 1000–2500 nm). The variation of fructose, glucose, sucrose, and total water-soluble sugar (TWSS) content was analyzed using HPLC with a reference method during fungal infection in strawberry. The feasibility of quantifying sugar constituents relevant to the different stages of decay in strawberry was evaluated using NIR-HSI with key wavelengths selected via successive projection algorithm. The results showed that the predicted performance of TWSS content was acceptable within 2 and 2.603 for RPD, respectively. Five to seven key wavelengths were obtained based on sugar constituents, and excellent performance for classification accuracy among the three stages of decay was 89.4 to 95.4% for calibration and 87.0 to 94.4% for prediction, respectively. This rapid approach provides a new strategy for the selection of key wavelengths to detect the decay and sugar constituents in strawberries.
关键词： Strawberry,Key wavelength,Decay,Sugar content,Hyperspectral imaging
更新于2025-09-23 15:23:52
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Visual detection of the moisture content of tea leaves with hyperspectral imaging technology
摘要： Hitherto, the rapid and nondestructive determination of the moisture content of tea leaves is still an unresolved issue because the upward facing surfaces of tea leaves lying on a conveyor belt are randomly chosen by the collapse of the leaves onto their front side or back side. To study the above issue, hyperspectral images of both the front side and back side of tea leaves on a conveyor belt were captured in the lab to simulate a practical production environment, and LS-SVR models with Rv2 values of 0.951 and 0.918 for the front side and back side, respectively, were established based on their characteristic spectral bands. To ensure that the spectrum of each pixel can be correctly imported into its corresponding model, a logistic regression classifier with a correct classification rate of 100 % was designed to identify the front side and back side of the leaves. Finally, a distribution map of the moisture content of the tea leaves was generated successfully according to the following steps: (1) Extracting the average spectrum of each leaf; (2) Identifying which side of the leaf the spectrum belongs to; (3) Importing the adjusted spectrum of each pixel into its corresponding regression model; and (4) Generating a distribution map of the moisture content. This research creatively provides a scheme for detecting the moisture content of tea leaves.
关键词： moisture content,front side,hyperspectral imaging,tea leaf,back side
更新于2025-09-23 15:23:52
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CubeDMA – Optimizing three-dimensional DMA transfers for hyperspectral imaging applications
摘要： Onboard computing is one of the principal needs in space-related technology in the recent years. In particular, onboard hyperspectral imaging (HSI) processing has advanced significantly. Due to advances in sensor technology, onboard HSI processing continuously meets new challenges related to increasing dataset size, limited processing time and limited communication links. High throughput and data reduction are crucial for satisfying real-time constraint and for preserving transmission bandwidth. For systems capable of accommodating a wide range of processing algorithms, there is a need for a flexible communication infrastructure that can provide fast access to/from memory in different access patterns. In this paper, existing FPGA-related Direct Memory Access (DMA) solutions have been evaluated, and a new DMA solution tailored for hyperspectral images has been proposed. Results show that the proposed DMA core, CubeDMA, handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient.
关键词： HSI cube,DMA,On-board processing,Direct memory access,Hyperspectral imaging
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Processing a New Hyperspectral Data Set for Target Detection and Atmospheric Compensation Algorithm Assessment: The RIT2017 Data Set
摘要： This paper introduces a new and challenging hyperspectral dataset to the remote sensing community called the 'RIT2017 Data Set' which can be used for the assessment of target detection algorithms. This dataset encompasses 90 targets in a background of up to 8 million pixels (or less if sub-setting). The same dataset can also be used for atmospheric compensation studies for it has identical sets of large panels in both the sun and full shadow. This paper briefly introduces the data collection campaign, the target objects, and addresses the radiometric fidelity of the imaging spectrometer data, which showed very good results. Lastly, the data is atmospherically compensated using an in-scene technique, which also showed fairly good results.
关键词： atmospheric compensation,physics-based modeling,hyperspectral imaging,target detection,radiative transfer
更新于2025-09-23 15:23:52
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hybrid Parametric - Nonparametric Target Detector for Hyperspectral Images
摘要： In this work a novel target detector is proposed that is nonparametric in terms of conditional probability density function (pdf) estimation and parametric with respect to the target strength of the additive model it relies upon. The variable bandwidth kernel density estimator is employed to estimate the conditional pdfs, whereas the target strength is estimated via the Maximum Likelihood approach. Experimental results over real hyperspectral data show that the detector succeeds in detecting target objects embedded in a complex background and in providing reasonable estimates for the target strengths.
关键词： nonparametric approach,kernel density estimation,additive model,target detection,Hyperspectral imaging
更新于2025-09-23 15:23:52