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Estimation of the number of endmembers in hyperspectral data using a weight-sequence geometry method
摘要： The terrestrial reflection or emission spectrum obtained by the remote sensor is recorded in units of pixels. In most cases, a pixel usually contains many types of terrains. This pixel is a mixed pixel, and each of the terrains in the mixed pixels is called 'endmember'. Estimating the number of endmembers is a significant step in many hyperspectral data mining techniques, such as target classification and endmember extraction. The paper proposes a separative detection method by the use of a weight-sequence geometry to estimate the number of endmembers. This method projects the spectral matrix into the orthogonal subspace by eigenvalue decomposition at first. Then, on the basis of the normalized eigenvalue sequence, the separative detection method innovatively uses a geometric criterion to find the separation point between the main factors and minor factors. Finally, the number of endmembers is determined by the sequence of the 'separation point'. Validation through a series of simulated and real hyperspectral data, it indicates that the proposed method can accurately and rapidly detect the number of endmembers in the hyperspectral data without any prior information. In addition, the new method is also applicable to the ultra-high resolution remote spectral data in the future.
关键词： number of endmembers,Hyperspectral data,separative detection method,hyperspectral unmixing
更新于2025-09-23 15:22:29
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Silk fibroin enabled optical fiber methanol vapor sensor
摘要： The linear mixture model (LMM) plays a crucial role in the spectral unmixing of hyperspectral data. Under the assumption of LMM, the solution with the minimum reconstruction error is considered to be the ideal endmember. However, for practical hyperspectral data sets, endmembers that enclose all the pixels are physically meaningless due to the effect of noise. Therefore, in many cases, it is not sufficient to consider only the reconstruction error, some constraints (for instance, volume constraint) need to be added to the endmembers. The two terms can be considered as serving two forces: minimizing the reconstruction error forces the endmembers to move outward the simplex while the endmember constraint acts in the opposite direction by driving the endmembers to move inward so as to constrain the volume to be smaller. Many existing methods obtain their solution just by balancing the two contradictory forces. The solution acquired in this way can not only minimize the reconstruction error but also be physically meaningful. Interestingly, we find, in this paper, that the two forces are not completely contradictory with each other, and the reconstruction error can be further reduced without changing the volume of the simplex. And more interestingly, our method can further optimize the solution provided by all the endmember extraction methods (both endmember selection methods and endmember generation methods). After optimization, the final endmembers outperform the initial solution in terms of reconstruction error as well as accuracy. The experiments on simulated and real hyperspectral data verify the validation of our method.
关键词： Hyperspectral data,volume,endmember,LMM,simplex
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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A study on weighing a semi-labelled sample using spectral and spatial context information
摘要： Classification of spectrally similar objects is a hard task, mainly when using moderate resolution data. Even though hyperspectral data are a useful source of information, the Hughes phenomenon is highlighted when limited number of training samples are used. For data classification and to mitigate this drawback, the number of training samples needs to be increased in the methodology. In this study, we investigated the estimation of the weights of semi-labelled samples using spectral and spatial context information by relaxation process in a two-steps methodology. The weights of semi-labelled samples in parametric classifier were estimated iteratively in the first step using spectral information only. In the second step, addition of spatial information was done by a relaxation process. This study investigated a more refined approach, improved by the inclusion of spatial context information in the relaxation process. The aim of this work was to mitigate the Hughes phenomenon and improve the separation of similar classes. The proposed methodology was tested using the data (hyperspectral image) from a study area, where the land cover classes are spectrally similar and the accurate separation of these classes was a hard task. Even though several experiments were performed, only a selected number of representative experiments are presented in this work. The results showed that the inclusion of context information can be used for the successful mitigation of the Hughes phenomenon allowing almost twice the number of bands used and increase the classification overall accuracy by up to 8%.
关键词： Hughes phenomenon,hyperspectral data,spectral and spatial context information,classification,relaxation process
更新于2025-09-19 17:13:59
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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 - Small Size Class Preserving Classification Based on Segmentation for Hyperspectral Data
摘要： Noises in hyper spectral data make it difficult to accurately classify the domains. In order to solve this problem, some filtering methods are proposed; however, filtering processes disturb small size classes and sharp boundaries. To protect the domain boundaries in hyperspectral data classification, we applied normalized cuts segmentation in the preprocessing before classification. The proposed methodologies show higher OA and AA, which means that the domain boundaries are maintained, and classification accuracies of small classes are improved. We also found that the classification accuracies are not sensitive to the number of clusters. The proposed methodology is useful for the combination of smoothing filters that show high classification performances.
关键词： normalized cuts,Hyperspectral data,small size class,edge-preserving filter,segmentation
更新于2025-09-10 09:29:36
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Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression
摘要： In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China.
关键词： alkalinity and salinity,PLSR model,hyperspectral data,soil
更新于2025-09-10 09:29:36
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[Lecture Notes in Electrical Engineering] Microelectronics, Electromagnetics and Telecommunications Volume 521 (Proceedings of the Fourth ICMEET 2018) || Assessment of EO-1 Hyperion Imagery for Crop Discrimination Using Spectral Analysis
摘要： This paper outlines the research objectives to discriminate crop species using pure spectral-spatial reflectance of EO-1 Hyperion imagery. Vigorous encroachment in remote sensing unlocks the new avenues to investigate the hyper-spectral imagery for analysis and implication for crop-type classification and agricultural management. The investigated crop species were namely Sorghum, Wheat, and cotton located in West zone of Aurangabad, Maharashtra, India. The preprocessing algorithm namely quick atmospheric correction (QUAC) was applied to calibrate bad bands and construct precise data for crop discrimination. The machine learning classifiers applied to identify the pixels having a significant difference in pure spectral signatures based on Ground Control Point (GCP) and image spectral responses. The investigation was based on a binary encoding (BE) and support vector machine (SVM) learning approach in order to discriminate crop types. Crop discrimination followed land cover classes gives 73.35% accuracy using BE and SVM with polynomial third-degree order gives overall accuracy 90.44%. These results show that satellite data with 30 m spatial resolution (Hyperion) are able to identify crop species using Environment for Visualizing Images (ENVI) open source software.
关键词： Atmospheric correction,Support vector machine,A spectral signature,Hyperspectral data
更新于2025-09-10 09:29:36
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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 - Introducing a Framework of Self-Organizing Maps for Regression of Soil Moisture with Hyperspectral Data
摘要： In this paper, we introduce a framework to solve regression problems based on high-dimensional and small datasets. This framework involves two self-organizing maps (SOM) and combines unsupervised with supervised learning. We investigate the impacts of SOM hyperparameters on the regression performance and compare the results of the SOM framework with two established regressors on a measured dataset. The derived results reveal the potential of the SOM framework. Finally, we propose further research aspects for the SOM framework to analyze its capabilities and limitations. We have published our dataset in [1] to ensure the reproducibility of the results.
关键词： machine learning,regression,hyperspectral data,soil moisture,Self-organizing maps
更新于2025-09-10 09:29:36
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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 Dimensional Reduction Methods on Urban Vegegation Classification Performance Using Hyperspectral Data
摘要： In the context of urban vegetation, hyperspectral imagery allows to discriminate biochemical properties of land surfaces. In this study, we test several dimension reductions to evaluate capacities of hyperspectral sensors to characterize tree families. The goal is to evaluate if a selection of differentiated and uncorrelated vegetation indices is an efficient method to reduce the dimension of hyperspectral images. This method is compared with conventional MNF and ACP approaches, and assessed on tree vegetation classifications performed using SVM classifier on two datasets at 4m and 8m spatial resolution. Results show that MNF combined with SVM classification is the better method to reduce hyperspectral dimension.
关键词： Urban vegetation,dimension reduction,SVM,hyperspectral data
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