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- 关键词
- 实验方案
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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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Deriving clear-sky longwave spectral flux from spaceborne hyperspectral radiance measurements: a case study with AIRS observations
摘要： Previous studies have shown that longwave (LW) spectral fluxes have unique merit in climate studies. Using Atmospheric Infrared Sounder (AIRS) radiances as a case study, this study presents an algorithm to derive the entire LW clear-sky spectral fluxes from spaceborne hyperspectral observations. No other auxiliary observations are needed in the algorithm. A clear-sky scene is identified using a three-step detection method. The identified clear-sky scenes are then categorized into different sub-scene types using information about precipitable water, lapse rate and surface temperature inferred from the AIRS radiances at six selected channels. A previously established algorithm is then used to invert AIRS radiances to spectral fluxes over the entire LW spectrum at 10 cm?1 spectral interval. Accuracy of the algorithms is evaluated against collocated Clouds and the Earth’s Radiant Energy System (CERES) observations. For nadir-view observations, the mean difference between outgoing longwave radiation (OLR) derived by this algorithm and the collocated CERES OLR is 1.52 Wm?2 with a standard deviation of 2.46 Wm?2. When the algorithm is extended for viewing zenith angle up to 45?, the performance is comparable to that for nadir-view results.
关键词： hyperspectral radiance measurements,longwave spectral flux,clear-sky detection,climate studies,AIRS
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - BRDF Effect on the Estimation of Canopy Chlorophyll Content in Paddy Rice from UAV-Based Hyperspectral Imagery
摘要： The bidirectional reflectance distribution function (BRDF) effect due to the surface reflectance anisotropy and variations in the solar and viewing geometry has been studied in the remote sensing community for several decades, and most attention was paid to the satellite sensors with large field of view (FOV), such as MODIS with a 110° FOV. With the development of unmanned aerial vehicle (UAV) technique, the imagery acquired at UAV platform provides important information about crop growth status, which is a promising and efficient approach for precise agriculture. However, few studies explored the BRDF effect in UAV images, especially for the sensors with small FOVs. This study investigated the BRDF effect on the estimation of canopy chlorophyll content (CCC) with the UHD 185 hyperspectral imagery (27° FOV) acquired at a UAV platform. Our results from a rice field-plot experiment demonstrated that the CCC was highly correlated to the red-edge chlorophyll index derived at five different view angles. However, the regression models were significantly different among these view angles. This implied that no single CCC estimation model can be applied to the whole image for CCC mapping. The findings suggest the BRDF effect should be considered for providing reliable and consistent CCC estimation.
关键词： Chlorophyll content,BRDF,Hyperspectral imagery,Paddy rice
更新于2025-09-09 09:28:46
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Unsupervised band selection based on artificial bee colony algorithm for hyperspectral image classification
摘要： Hyperspectral image (HSI), with hundreds of narrow and adjacent spectral bands, supplies plentiful information to distinguish various land-cover types. However, these spectral bands ordinarily contain a lot of redundant information, leading to the Hughes phenomenon and an increase in computing time. As a popular dimensionality reduction technology, band/feature selection is indispensable for HSI classification. Based on improved subspace decomposition (ISD) and the artificial bee colony (ABC) algorithm, this paper proposes a band selection technique known as ISD-ABC to address the problem of dimensionality reduction in HSI classification. Subspace decomposition is achieved by calculating the correlation coefficients between adjacent bands and using the visualization result of the HSI spectral curve. The artificial bee colony algorithm is first applied to optimize the combination of selected bands with the guidance of ISD and maximum entropy (ME). Using the selected band subset, support vector machine (SVM) with five-fold cross validation is applied for HSI classification. To evaluate the effectiveness of the proposed method, experiments are conducted on two AVIRIS datasets (Indian Pines and Salinas) and a ROSIS dataset (Pavia University). Three indices, namely, overall accuracy (OA), average accuracy (AA) and kappa coefficient (KC), are used to assess the classification results. The experimental results successfully demonstrate that the proposed method provides good classification accuracy compared with six other state-of-the-art band selection techniques.
关键词： dimensionality reduction,band selection,Hyperspectral image,ABC algorithm,subspace decomposition
更新于2025-09-09 09:28:46
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Hyperspectral Terahertz Tomography in Amplitude Contrast
摘要： Hyperspectral Terahertz Tomography in amplitude contrast is presented using a time-domain spectroscopy system in the spectral range 0.3 - 2.5 THz. The Fourier transformed signal data is used to reconstruct test objects’ cross-sections at multiple frequencies using standard filtered backprojection. The full hyperspectral set of images reconstructed at around 300 adjacent spectral points is used to trace the combined contribution of Beer-Lambert volume attenuation, Fresnel reflection losses and Rayleigh roughness scattering losses, which is in good overall agreement with the experimental results. The image quality for Styrofoam (refractive index around 1.02, attenuation coefficient < 1 mm-1) test objects is best in the range 0.8 - 2.0 THz depending on the porosity of the material.
关键词： THz,time-domain spectroscopy,hyperspectral imaging,Rayleigh roughness,hard-field tomography
更新于2025-09-09 09:28:46
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Data Augmentation for Hyperspectral Image Classification With Deep CNN
摘要： Convolutional neural network (CNN) has been widely used in hyperspectral imagery (HSI) classification. Data augmentation is proven to be quite effective when training data size is relatively small. In this letter, extensive comparison experiments are conducted with common data augmentation methods, which draw an observation that common methods can produce a limited and up-bounded performance. To address this problem, a new data augmentation method, named as pixel-block pair (PBP), is proposed to greatly increase the number of training samples. The proposed method takes advantage of deep CNN to extract PBP features, and decision fusion is utilized for final label assignment. Experimental results demonstrate that the proposed method can outperform the existing ones.
关键词： pattern classification,Convolutional neural network (CNN),hyperspectral imagery (HSI),data augmentation
更新于2025-09-09 09:28:46
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Self-Supervised Feature Learning With CRF Embedding for Hyperspectral Image Classification
摘要： The challenges in hyperspectral image (HSI) classification lie in the existence of noisy spectral information and lack of contextual information among pixels. Considering the three different levels in HSIs, i.e., subpixel, pixel, and superpixel, offer complementary information, we develop a novel HSI feature learning network (HSINet) to learn consistent features by self-supervision for HSI classification. HSINet contains a three-layer deep neural network and a multifeature convolutional neural network. It automatically extracts the features such as spatial, spectral, color, and boundary as well as context information. To boost the performance of self-supervised feature learning with the likelihood maximization, the conditional random field (CRF) framework is embedded into HSINet. The potential terms of unary, pairwise, and higher order in CRF are constructed by the corresponding subpixel, pixel, and superpixel. Furthermore, the feedback information derived from these terms are also fused into the different-level feature learning process, which makes the HSINet-CRF be a trainable end-to-end deep learning model with the back-propagation algorithm. Comprehensive evaluations are performed on three widely used HSI data sets and our method outperforms the state-of-the-art methods.
关键词： self-supervision,feature learning,convolutional neural network (CNN),Conditional random field (CRF),hyperspectral image (HSI) classification
更新于2025-09-09 09:28:46
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Chemical Colour Imaging and its Advantages by Deploying Hyperspectral Cameras for Industrial Applications
摘要： Chemical colour imaging (CCI) represents a new processing technology, which combines the essential advantages of basic technologies of chemical imaging and colour imaging (colour image processing) and makes chemical material properties accessible to a completely new range of users through new approaches to data processing. A dramatic simplification in handling, as well as the opportunity for real-time processing of highly complex camera data, are the keys to an extensive industrial use of this chemical camera technology. The abstraction of highly complex spectral information through chemical features makes handling of the cameras on a deep level accessible to the user and interpretable, even without a profound knowledge of the basic technologies. New aspects of dealing with chemical information arise and these accelerate continuous further development of chemical colour imaging technology.
关键词： chemical colour imaging,image processing,hyperspectral cameras,industrial applications,spectroscopy
更新于2025-09-09 09:28:46
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[Smart Innovation, Systems and Technologies] Information Systems and Technologies to Support Learning Volume 111 (Proceedings of EMENA-ISTL 2018) || A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines
摘要： Band selection is a great challenging task in the classi?cation of hyperspectral remotely sensed images HSI. This is resulting from its high spectral resolution, the many class outputs and the limited number of training samples. For this purpose, this paper introduces a new ?lter approach for dimension reduction and classi?cation of hyperspectral images using information theoretic (normalized mutual information) and support vector machines SVM. This method consists to select a minimal subset of the most informative and relevant bands from the input datasets for better classi?cation ef?ciency. We applied our proposed algorithm on two well-known benchmark datasets gathered by the NASA’s AVIRIS sensor over Indiana and Salinas valley in USA. The experimental results were assessed based on different evaluation metrics widely used in this area. The comparison with the state of the art methods proves that our method could produce good performance with reduced number of selected bands in a good timing.
关键词： Support vector machines,Classi?cation,Dimension reduction,Band selection,Hyperspectral images,Normalized mutual information
更新于2025-09-09 09:28:46
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Estimation of paddy rice leaf area index using machine learning methods based on hyperspectral data from multi-year experiments
摘要： The performance of three machine learning methods (support vector regression, random forests and artificial neural network) for estimating the LAI of paddy rice was evaluated in this study. Traditional univariate regression models involving narrowband NDVI with optimized band combinations as well as linear multivariate calibration partial least squares regression models were also evaluated for comparison. A four year field-collected dataset was used to test the robustness of LAI estimation models against temporal variation. The partial least squares regression and three machine learning methods were built on the raw hyperspectral reflectance and the first derivative separately. Two different rules were used to determine the models’ key parameters. The results showed that the combination of the red edge and NIR bands (766 nm and 830 nm) as well as the combination of SWIR bands (1114 nm and 1190 nm) were optimal for producing the narrowband NDVI. The models built on the first derivative spectra yielded more accurate results than the corresponding models built on the raw spectra. Properly selected model parameters resulted in comparable accuracy and robustness with the empirical optimal parameter and significantly reduced the model complexity. The machine learning methods were more accurate and robust than the VI methods and partial least squares regression. When validating the calibrated models against the standalone validation dataset, the VI method yielded a validation RMSE value of 1.17 for NDVI(766,830) and 1.01 for NDVI(1114,1190), while the best models for the partial least squares, support vector machine and artificial neural network methods yielded validation RMSE values of 0.84, 0.82, 0.67 and 0.84, respectively. The RF models built on the first derivative spectra with mtry = 10 showed the highest potential for estimating the LAI of paddy rice.
关键词： paddy rice,machine learning,remote sensing,leaf area index,hyperspectral data
更新于2025-09-09 09:28:46
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Sparse Dictionary Learning for Blind Hyperspectral Unmixing
摘要： Dictionary learning (DL) has been successfully applied to blind hyperspectral unmixing due to the similarity of underlying mathematical models. Both of them are linear mixture models and quite often sparsity and nonnegativity are incorporated. However, the mainstream sparse DL algorithms are crippled by the difficulty in prespecifying suitable sparsity. To solve this problem, this paper proposes an efficient algorithm to find all paths of the 1-regularization problem and select the best set of variables for the final abundances estimation. Based on the proposed algorithm, a DL framework is designed for hyperspectral unmixing. Our experimental results indicate that our method performs much better than conventional methods in terms of DL and hyperspectral data reconstruction. More importantly, it alleviates the difficulty of prescribing the sparsity.
关键词： sparse coding,Dictionary learning (DL),hyperspectral unmixing,1-regularization,path algorithm
更新于2025-09-09 09:28:46