- 标题
- 摘要
- 关键词
- 实验方案
- 产品
过滤筛选
- 2018
- Fruit defects
- Jujube
- Principal component analysis
- Hyperspectral imaging
- hyperspectral images
- spectral and spatial features
- classification
- SVM
- mutual information
- GLCM
- Optoelectronic Information Science and Engineering
- Mohammed V University in Rabat
- Southern Taiwan University of Science and Technology
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - HSVCNN: CNN-Based Hyperspectral Reconstruction from RGB Videos
摘要： Hyperspectral video acquisition usually requires high complexity hardware and reconstruction algorithms. In this paper, we propose a low complexity CNN-based method for hyperspectral reconstruction from ubiquitous RGB videos, which effectively exploits the temporal redundancies within RGB videos and generates high-quality hyperspectral output. Specifically, given an RGB video, we first design an efficient motion compensation network to align the RGB frames and reduce the large motion. Then, we design a temporal-adaptive fusion network to exploit the inter-frame correlation. The fusion network has the ability to determine the optimum temporal dependency within successive frames, which further promotes the hyperspectral reconstruction fidelity. Preliminary experimental results validate the superior performance of the proposed method over previous learning-based methods. To the best of our knowledge, this is the first time that RGB videos are utilized for hyperspectral reconstruction through deep learning.
关键词： Hyperspectral reconstruction,temporal-adaptive fusion,RGB videos,motion compensation
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 - Using Sentinel-2 Imagery to Track Changes Produced by Xylella Fastidiosa in Olive Trees
摘要： This paper attempts to provide an understanding of the potential application of Sentinel-2 imagery for the monitoring and detection of disease symptoms caused by Xylella fastidiosa (Xf) in olive trees. A time series data of 188 Sentinel-2a images collected over the last two years was used to analyse the temporal trends in areas with Xf infected olive trees in Puglia, Southern Italy. The robustness of different physiological and structural hyperspectral indices was evaluated as an early indicator of Xf symptoms. Three validation sites for Sentinel-2a products were hence established over olive orchards in the Xf-infected zone in two different years (2016 and 2017) and overflown with a hyperspectral sensor to acquire high spatial resolution images (50 cm). Disease incidence and severity levels were recorded for more than 3300 olive trees in 18 orchards. Results demonstrate the capability of temporal Sentinel-2a was able to detect and discriminate between high and medium Xf incidence, reaching the maximum differences during the summer season. Among all the vegetation indices evaluated from Sentinel-2 imagery, OSAVI showed superior performance for detecting Xf incidence trends and OSAVI1510 for detecting changes in Xf severity levels.
关键词： Sentinel-2,hyperspectral,olive-tree,temporal trend,die-back,Xylella fastidiosa
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
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Quantitative cosmetic evaluation of long-lasting foundation using multispectral imaging
摘要： Background: We tried to search appropriate wavelength to quantitatively evaluate the ability of long- lasting foundation using a hyperspectral imager (HSI) which can simultaneously measure position and wavelength information. Materials and methods: A good reputable long- lasting foundation was applied to the skin of 10 healthy volunteers. Their skin was measured by our newly developed HSI every 2 hours from immediately after application to 6 hours. The application state of the foundation was quantified using the standard deviation of reflectance. Results: A high correlation between standard deviation and the application state of the foundation was confirmed at many wavelengths. In particular, it was suggested that by using the standard deviation of 800 nm, the application state of the foundation can be evaluated quantitatively without depending on the subject’s oxygen saturation level. Conclusion: By quantitatively evaluating the cosmetic- applied skin by our system, further efficiency improvement of the volunteer experiment is expected.
关键词： cosmetic evaluation,spectrum analysis,long-lasting foundation,hyperspectral imaging
更新于2025-09-23 15:23:52
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Classification of Sugar Beets Based on Hyperspectral and Extreme Learning Machine Methods
摘要： Sugar beet varieties were classified based on hyperspectral technology and the Extreme Learning Machine (ELM) algorithm. The influences of seven pretreatment methods, namely, Savitzky-Golay smoothing (SG), the first derivative (FD) method, SG smoothing combined with the FD method (SG-FD), logarithmic transformation (LT), LT combined with the FD method (LT-FD), the standard normal variate (SNV) method, and SNV combined with the FD method (SNV-FD), on the recognition performance of the ELM model were analyzed to select the best pretreatment method. To simplify the input variables, the standard deviation peak method was used to extract the feature bands for different preprocessed spectral data. The experimental results showed that for different pretreatment methods, the recognition rates of sugar beet varieties by ELM models were all over 80%. Additionally, the combination of different pretreatment methods and FD effectively improved the signal-to-noise ratio and enhanced the accuracy and stability of spectral models. Overall, the recognition accuracy of the ELM models established based on the feature bands was better than that established based on all bands, which suggests that the feature bands extracted by the standard deviation peak method are effective. Based on the SG-FD pretreatment method, the ELM models established using all bands and feature bands both achieved the highest recognition effect. Specifically, the recognition rates of the prediction sets were 93.94% and 95.45%, respectively.
关键词： Standard deviation peak method,ELM,Different pretreatment methods,Sugar beet variety,Hyperspectral
更新于2025-09-23 15:23:52
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Multiple deep-belief-network-based spectral-spatial classification of hyperspectral images
摘要： A deep-learning-based feature extraction has recently been proposed for HyperSpectral Images (HSI) classification. A Deep Belief Network (DBN), as part of deep learning, has been used in HSI classification for deep and abstract feature extraction. However, DBN has to simultaneously deal with hundreds of features from the HSI hyper-cube, which results into complexity and leads to limited feature abstraction and performance in the presence of limited training data. Moreover, a dimensional-reduction-based solution to this issue results in the loss of valuable spectral information, thereby affecting classification performance. To address the issue, this paper presents a Spectral-Adaptive Segmented DBN (SAS-DBN) for spectral-spatial HSI classification that exploits the deep abstract features by segmenting the original spectral bands into small sets/groups of related spectral bands and processing each group separately by using local DBNs. Furthermore, spatial features are also incorporated by first applying hyper-segmentation on the HSI. These results improved data abstraction with reduced complexity and enhanced the performance of HSI classification. Local application of DBN-based feature extraction to each group of bands reduces the computational complexity and results in better feature extraction improving classification accuracy. In general, exploiting spectral features effectively through a segmented-DBN process and spatial features through hyper-segmentation and integration of spectral and spatial features for HSI classification has a major effect on the performance of HSI classification. Experimental evaluation of the proposed technique on well-known HSI standard data sets with different contexts and resolutions establishes the efficacy of the proposed techniques, wherein the results are comparable to several recently proposed HSI classification techniques.
关键词： hyperspectral image classification,support vector machine,deep belief network,segmentation
更新于2025-09-23 15:23:52
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Hyperspectral Mixed Denoising via Spectral Difference-Induced Total Variation and Low-Rank Approximation
摘要： Exploration of multiple priors on observed signals has been demonstrated to be one of the effective ways for recovering underlying signals. In this paper, a new spectral difference-induced total variation and low-rank approximation (termed SDTVLA) method is proposed for hyperspectral mixed denoising. Spectral difference transform, which projects data into spectral difference space (SDS), has been proven to be powerful at changing the structures of noises (especially for sparse noise with a specific pattern, e.g., stripes or dead lines present at the same position in a series of bands) in an original hyperspectral image (HSI), thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. In addition, because the neighboring pixels are highly correlated and the spectra of homogeneous objects in a hyperspectral scene are always in the same low-dimensional manifold, we are inspired to combine total variation and the nuclear norm to simultaneously exploit the local piecewise smoothness and global low rankness in SDS for mixed noise reduction of HSI. Finally, the alternating direction methods of multipliers (ADMM) is employed to effectively solve the SDTVLA model. Extensive experiments on three simulated and two real HSI datasets demonstrate that, in terms of quantitative metrics (i.e., the mean peak signal-to-noise ratio (MPSNR), the mean structural similarity index (MSSIM) and the mean spectral angle (MSA)), the proposed SDTVLA method is, on average, 1.5 dB higher MPSNR values than the competitive methods as well as performing better in terms of visual effect.
关键词： ADMM,total variation,hyperspectral mixed denoising,low-rank approximation,spectral difference space
更新于2025-09-23 15:23:52
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Harnessing the synergy between upconverting nanoparticles and lanthanide complexes in a multi-wavelength responsive hybrid system
摘要： We prepared a hybrid system composed of a continuous film of dinuclear lanthanide complex [Ln2bpm(tfaa)6] (Ln = Tb or Eu) and upconverting nanoparticles (UCNPs) using a straightforward drop-cast methodology. The system displayed visible emission under near-infrared (NIR) excitation, simultaneously stemming from sub-10-nm UCNPs and [Ln2] complexes, the latter species being otherwise directly excitable only using UV-blue radiation. In light of the results of steady-state – including power-dependent – and time-resolved optical measurements, we identified the radiative, primarily ligand-mediated nature of the energy transfer from Tm3+ ions in the UCNPs-to-Ln3+ ions in the complexes. Hyperspectral mapping and electron microscopy observations of the surface of the hybrid system confirmed the continuous and concomitant distribution of UCNPs and lanthanide complexes over the extensive composite films. Key features of the hybrid system are the simultaneous UV-blue and NIR light harvesting capabilities and their ease of preparation. These traits render the presented hybrid system a formidable candidate for the development of photoactivated devices capable to operate under multiple excitation wavelength and to transduce the absorbed light into narrow, well-defined spectral regions.
关键词： hybrid system,complex,energy transfer,lanthanide,films,upconverting nanoparticles,hyperspectral imaging
更新于2025-09-23 15:23:52
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A review on graph-based semi-supervised learning methods for hyperspectral image classification
摘要： In this article, a comprehensive review of the state-of-art graph-based learning methods for classification of the hyperspectral images (HSI) is provided, including a spectral information based graph semi-supervised classification and a spectral-spatial information based graph semi-supervised classification. In addition, related techniques are categorized into the following sub-types: (1) Manifold representation based Graph Semi-supervised Learning for HSI Classification (2) Sparse representation based Graph Semi-supervised Learning for HSI Classification. For each technique, methodologies, training and testing samples, various technical difficulties, as well as performances, are discussed. Additionally, future research challenges imposed by the graph-based model are indicated.
关键词： Image classification,Hyperspectral images,Semi-supervised learning,Graph-based learning
更新于2025-09-23 15:23:52