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- 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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Quantitative Estimation of Biomass of Alpine Grasslands Using Hyperspectral Remote Sensing
摘要： In order to promote the application of hyperspectral remote sensing in the quanti?cation of grassland areas’ physiological and biochemical parameters, based on the spectral characteristics of ground measurements, the dry AGB and multisensor satellite remote sensing data, including such methods as correlation analysis, scaling up, and regression analysis, were used to establish a multiscale remote sensing inversion model for the alpine grassland biomass. The feasibility and effectiveness of the model were veri?ed by the remote sensing estimation of a time-space sequence biomass of a plateau grassland in northern Tibet. The results showed that, in the ground spectral characteristic parameters of the grassland’s biomass, the original wave bands of 550, 680, 860, and 900 nm, as well as their combination form, had a good correlation with biomass. Also, the remote sensing biomass estimation model established on the basis of the two spectral characteristics (VI2 and Normalized Difference Vegetation Index [NDVI]) had a high inversion accuracy and was easy to realize, with a ?tting R2 of 0.869 and an F test value of 92.6. The biomass remote sensing estimate after scale transformation had a standard deviation of 53.9 kg/ha from the ?tting model established by MODIS NDVI, and the estimation accuracy was 89%. Therefore, it displayed the ability to realize the estimation of large-scale and long-time sequence remote sensing biomass. The veri?cation of the model’s accuracy, comparison of the existing research results of predecessors, and analysis of the regional development background demonstrated the effectiveness and feasibility of this method.
关键词： biomass,spectral characteristic parameters,alpine grassland,multiscale,hyperspectral remote sensing
更新于2025-09-23 15:21:01
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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 - Sun Induced Fluorescence Calibration and Validation for Field Phenotyping
摘要： Reliable measurements of Sun Induced Fluorescence (SIF) require a good instrument characterization as well as a complex processing chain. In this paper, we summarize the state of the art SIF retrieval methods and measurements platforms for field phenotyping. Furthermore, we use HyScreen, hyperspectral-imaging system for top of canopy measurements of SIF, as an example of the instrument requirements, data process, and data validation needed to obtain reliable measurements of SIF.
关键词： Sun Induced fluorescence,hyperspectral measurements,retrievals method,field spectrometers,field phenotyping
更新于2025-09-23 15:21:01
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A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
摘要： Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent the limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can greatly benefit from spatial regularization strategies. However, existing spatial regularization strategies lead to large-scale non-smooth optimization problems. Thus, efficiently introducing spatial context in the unmixing problem remains a challenge and a necessity for many real world applications. In this letter, a novel multiscale spatial regularization approach for sparse unmixing is proposed. The method uses a signal-adaptive spatial multiscale decomposition based on segmentation and oversegmentation algorithms to decompose the unmixing problem into two simpler problems: one in an approximation image domain and another in the original domain. Simulation results using both synthetic and real data indicate that the proposed method outperforms the state-of-the-art total variation-based algorithms with a computation time comparable to that of their unregularized counterparts.
关键词： spatial regularization,superpixels,Hyperspectral data,sparse unmixing,multiscale
更新于2025-09-23 15:21:01
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Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm
摘要： Hyperspectral image classi?cation is a hot issue in the ?eld of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classi?cation method that is used to process image data. In this paper, an ef?cient hyperspectral image classi?cation method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classi?er to improve the classi?cation ef?ciency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classi?cation. Then, Q-statistic is used to select base classi?ers. Finally, the results are obtained by using the voting method. Three simulation examples, classi?cation of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.
关键词： extreme learning machine,rotation forest,hyperspectral image classi?cation,Q-statistic
更新于2025-09-23 15:21:01
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[IEEE 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - JeJu, Korea (South) (2018.6.24-2018.6.26)] 2018 IEEE International Conference on Consumer Electronics - Asia (ICCE-Asia) - Iterative Normalized Matched Filtering for Detection of Chemical Agents in Hyperspectral Imaging
摘要： Hyperspectral imaging (HSI) can be used to detect a harmful chemical agents’ (CAs’) cloud from a long distance. A normalized matched filter (NMF) is one of the best algorithms to detect CAs in the atmosphere with perfectly known statistics of the background. However, if the background are affected by a CA’s signal, (that is a contamination condition) the performance of the NMF detector is degraded. To design an NMF detector that is robust to contamination, we propose an iterative normalized matched filter (INMF). The proposed algorithm extracts CA-off spectra from the contaminated background spectra dataset using a contaminated NMF detector. And the NMF detector is designed using the extracted CA-off background spectra and this procedure repeats until convergence. Simulation results demonstrate that the proposed algorithm significantly improves the detection performance.
关键词： Gas detection,Normalized Matched Filter,Hyperspectral Image
更新于2025-09-23 15:21:01
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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 - Evaluation of Different Regularization Methods for the Extreme Learning Machine Applied to Hyperspectral Images
摘要： During recent years, many regularization techniques have been proposed to deal with ill-posed problems related to hyperspectral image classification, in which the limited number of training samples contrasts with the very high spectral dimensionality. However, the intrinsic structure of a hyperspectral image often depends on the specific scene and spectrometer, although regularizers like Ridge, LASSO, etc, have been widely used in practical applications. Instead of imposing these regularizers to the probabilistic output of a classifier, this work evaluates the use of extreme learning machines (ELM) with output weights of a single-hidden layer feed-forward neural network (SLFN) regularized with Ridge and LASSO priors, respectively. Experimental results with several real hyperspectral images are conducted to compare the performance and adaptation of these two regularizers with the original ELM in classification scenarios.
关键词： Ridge,LASSO,regularization,hyperspectral image classification,extreme learning machine
更新于2025-09-23 15:21:01
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Long-Term Agroecosystem Research in the Central Mississippi River Basin: Hyperspectral Remote Sensing of Reservoir Water Quality
摘要： In situ methods for estimating water quality parameters would facilitate efforts in spatial and temporal monitoring, and optical reflectance sensing has shown potential in this regard, particularly for chlorophyll, suspended sediment, and turbidity. The objective of this research was to develop and evaluate relationships between hyperspectral remote sensing and lake water quality parameters—chlorophyll, turbidity, and N and P species. Proximal hyperspectral water reflectance data were obtained on seven sampling dates for multiple arms of Mark Twain Lake, a large man-made reservoir in northeastern Missouri. Aerial hyperspectral data were also obtained on two dates. Water samples were collected and analyzed in the laboratory for chlorophyll, nutrients, and turbidity. Previously reported reflectance indices and full-spectrum (i.e., partial least squares regression) methods were used to develop relationships between spectral and water quality data. With the exception of dissolved NH3, all measured water quality parameters were strongly related (R2 ≥ 0.7) to proximal reflectance across all measurement dates. Aerial hyperspectral sensing was somewhat less accurate than proximal sensing for the two measurement dates where both were obtained. Although full-spectrum calibrations were more accurate for chlorophyll and turbidity than results from previously reported models, those previous models performed better for an independent test set. Because extrapolation of estimation models to dates other than those used to calibrate the model greatly increased estimation error for some parameters, collection of calibration samples at each sensing date would be required for the most accurate remote sensing estimates of water quality.
关键词： water quality,Mark Twain Lake,partial least squares regression,chlorophyll,hyperspectral remote sensing,nutrients,turbidity
更新于2025-09-23 15:21:01
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Parallel K-Means Clustering for Brain Cancer Detection Using Hyperspectral Images
摘要： The precise delineation of brain cancer is a crucial task during surgery. There are several techniques employed during surgical procedures to guide neurosurgeons in the tumor resection. However, hyperspectral imaging (HSI) is a promising non-invasive and non-ionizing imaging technique that could improve and complement the currently used methods. The HypErspectraL Imaging Cancer Detection (HELICoiD) European project has addressed the development of a methodology for tumor tissue detection and delineation exploiting HSI techniques. In this approach, the K-means algorithm emerged in the delimitation of tumor borders, which is of crucial importance. The main drawback is the computational complexity of this algorithm. This paper describes the development of the K-means clustering algorithm on different parallel architectures, in order to provide real-time processing during surgical procedures. This algorithm will generate an unsupervised segmentation map that, combined with a supervised classification map, will offer guidance to the neurosurgeon during the tumor resection task. We present parallel K-means clustering based on OpenMP, CUDA and OpenCL paradigms. These algorithms have been validated through an in-vivo hyperspectral human brain image database. Experimental results show that the CUDA version can achieve a speed-up of ~150× with respect to a sequential processing. The remarkable result obtained in this paper makes possible the development of a real-time classification system.
关键词： unsupervised clustering,brain cancer detection,Graphics Processing Units (GPUs),OpenCL,CUDA,K-means,OpenMP,hyperspectral imaging
更新于2025-09-23 15:21:01
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Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan
摘要： Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.
关键词： total suspended solids,partial least squares regression,irrigation ponds,hyperspectral,chlorophyll-a
更新于2025-09-23 15:21:01
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Prediction of cadmium concentration in brown rice before harvest by hyperspectral remote sensing
摘要： Cadmium (Cd) contaminated rice has become a global food security issue. Hyperspectral remote sensing can do rapid and nondestructive monitoring of environmental stress in plant. To realize the nondestructive detection of Cd in brown rice before harvest, the leaf spectral reflectance of rice exposed to six different levels of Cd stress was measured during the whole life stages. In addition, the dry weight of rice grain and Cd concentrations in brown rice were measured after harvest. The impact of Cd stress on the quantity and the quality of rice grain and on the leaf reflectance of rice was analyzed, and hyperspectral estimation models for predicting the Cd content in brown rice during three growth stages were established. The results showed that rice plants can impact the quality of the brown rice seriously, even if the impact on the quantity was not significant. All the established models had the capability to estimate Cd concentrations in brown rice (R2 > 0.598), and the best performance model, with the R2 value of 0.873, was use first derivative spectrum of booting stage as variable. It was concluded that the hyperspectral of rice leaves provides a new insight to predict Cd concentration in brown rice before harvest.
关键词： Derivative transformation,Brown rice,Booting stage,Before harvest,Hyperspectral,Cd concentration
更新于2025-09-23 15:21:01