hyperspectral data
The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters
... During acquisition periods, aerosol was mainly distributed in the fine mode, showing a non negligible imaginary part of the refractive index, with the exception of 4 May 2011 and 25 April 2013. The NRMSE and the SSV for ...
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Hyperspectral Data Dimensionality Reduction Using Hybrid Approach
... the hyperspectral data is not isotropic it means the noise radiation reaches a location from all direction with equal ...consecutive data reduction ...the data represented with correlation ...
5
Bands Regrouping of Hyperspectral Data Based on Spectral Correlation Matrix Analysis
... Hyperspectral data contains a huge amount of spectral data distinctive in spectral resolution, which allows identification of each pixel based on its spectral ...of data increases as spectral ...
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Spatial-Spectral Manifold Embedding of Hyperspectral Data
... To assess the effectiveness of the proposed SSME in hyperspec- tral embedding task, classification is selected to be a potential strategy (Gao et al., 2020). In our case, a simple but efficient classifier: nearest ...
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Assessment of target detection limits in hyperspectral data
... Abstract: Hyperspectral remote sensing data can be used for civil and military applications to detect and classify target objects that cannot be reliably separated using broadband ...Airborne ...
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Adaptive Grouping Distributed Compressive Sensing Reconstruction of Plant Hyperspectral Data
... There is a high spectral correlation for plant hyperspectral data. The joint sparse model based on spectral characteristics can not only improve the fidelity of reconstructed plant hyperspectral ...
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Data Fusion for Urban Feature Extraction from LiDAR and Hyperspectral Data
... LIDAR data may improve classification results. In Lidar data object detection is possible as well as prediction about height of ...of hyperspectral and LiDAR data can enhance overall detection ...
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A Visualization Method for Isosurface of Hyperspectral Data Combining the Spatial and Spectral Dimensions
... visual hyperspectral data is to turn it to an imaging display problem in the spatial domain, and the traditional researches mainly focused on visualizing hyperspectral data through color ...
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Supervised machine learning of fullcube hyperspectral data
... The Gini importance (mean decrease in impurity) measure can be used to rank the features according to their importance in the models ability for class separation. Alternatively, the mean decrease in accuracy could be ...
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Development and Applications of Machine Learning Methods for Hyperspectral Data
... In hyperspectral regression, many available datasets include only a few labeled ...of hyperspectral data gets increasingly affordable, reference data, such as soil moisture point measurements, ...
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Hyperspectral Data Classification Improved By Minimum Spanning Forests
... This work investigates the use of spectral and spatial information to classify hyperspectral data. A three-stage supervised classifier is employed to determine the class for each pixel. An initial ...
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A Self-Improving Convolution Neural Network for the Classification of Hyperspectral Data
... Abstract—In this paper, a self-improving convolutional neural network (CNN)-based method is proposed for the classification of hyperspectral data. This approach solves the so-called curse of dimensionality ...
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Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data
... Freshwater resources are regarded as the foundation of urban development and assure the sustainable prosper- ity of the city. The contaminations of fresh water in reservoirs can threaten safety of people directly and ...
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The novel method for LAI inversion using Lidar and hyperspectral data
... using Hyperspectral remote sensing im- ...LiDAR data and explored the method of LAI retrieval based on LiDAR data and Hyperspectral ...
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Investigating the utility of oblique tree-based ensembles for the classification of hyperspectral data
... As previously indicated, the staircase or box-like decision boundary generated by univariate splits, as is the case with CART and RF, may not be optimal for the classification of highly correlated data, such as ...
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Illumination Invariant Deep Learning for Hyperspectral Data
... dataset. Hyperspectral datasets are of high dimensionality as the reflectance at each wavelength for a single pixel can be inter- preted as a separate ...of hyperspectral data is linked to problems ...
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Exploring Data Mining Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data
... truth data with some ...of data at a larger scale than individual trees is, while not ideal, still quite ...the hyperspectral data, in which there was most likely a larger ...
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Simultaneous Visualization and Segmentation of Hyperspectral Data Using Fuzzy K Means Clustering
... Hyperspectral data processing typically demands enormous computational resources in terms of storage, computation, and input/output throughputs, particularly when real-time processing is ...for ...
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Fast Atmospheric Correction Method for Hyperspectral Data
... for Hyperspectral data) [6,7], improved ATREM code; Tafkaa [8], based on ATREM. Most of these codes are designed for the specific satellite imaging systems, a certain spectral range, a set of spectral ...
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Spectral-spatial approaches for hyperspectral data classification
... sensing data are acquired by sensors on board aircraft, spacecraft or Unmanned Aerial Ve- hicle (UAV) platforms ...sensing data is defined as the width of wavelength interval and the number of ...example, ...
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