Hyperspectral image classification
Hyperspectral Image Classification Based on Hierarchical SVM Algorithm for Improving Overall Accuracy
... tral image classification. Hyperspectral image classification accuracy depends on the number of classes, training samples and features space ...The classification performance ...
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Hyperspectral Image Classification using Genetic Algorithm after Visualization using Image Fusion
... presents hyperspectral image classification using genetic algorithm after visualization using image fusion ...technique. Hyperspectral remote sensors collect image data for a ...
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Spectral-spatial Feature Extraction for Hyperspectral Image Classification
... for hyperspectral images, the random sampling is usually undertaken on the same ...the image and the testing samples will locate adjacent to ...supervised hyperspectral image ...
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Spatial and Spectral Nonparametric Linear Feature Extraction Method for Hyperspectral Image Classification
... the classification speed, particularly when the training sample size is small, namely the small sample size (SSS) ...sensed hyperspectral images (HSIs) are often with hundreds of measured features (bands) ...
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Hyperspectral Image Classification using Softcomputing Techniques: A Review
... The hyperspectral classification falls into two major categories such as spectral classification and the spatial ...spectral classification, the reflectance values of the pixels at different ...
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Uncertainty assessment of hyperspectral image classification: Deep learning vs random forest
... of classification accuracy, which can be used to locate and segregate unreliable pixel-level class allocations from reliable ...of classification approaches: unsupervised schemes using no training dataset ...
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A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... The Hyperspectral Images (HSI) acquired by remote sensors are characterized by hundreds of contiguous channels with high spectral ...resolution. Hyperspectral image classification is the ...
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Hyperspectral Image Classification For Based BEMD Multivariate Gray Module
... of Hyperspectral Image Classification Thus, hyperspectral imaging is concerned with the measurement, processing and analysis of spectra acquired from a given scene at a short, medium or long ...
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Infinite Latent Feature Selection Technique for Hyperspectral Image Classification
... of hyperspectral cube. The dataset in a hyperspectral cube format is converted into a map that correspondence between its pixel and the reflectance value to make feature extraction process ...into ...
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Hyperspectral Image Classification based on Dimensionality Reduction and Swarm Optimization Approach
... spectral-spatial classification approach is introduced to improve the classification accuracy of hyperspectral ...input image will be reduced in dimension by using Discriminant independent ...
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Hybrid Subspace Detection based on Spectral and Spatial Information for Effective Hyperspectral Image Classification
... If the spatial size S is very large, the calculation of covariance matrix is difficult using PCA due to memory management issue [7]. Furthermore, PCA be unsuccessful to catch the individual contribution of each of the F ...
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Extreme sparse multinomial logistic regression : a fast and robust framework for hyperspectral image classification
... the classification results and consuming time of the proposed methods with CNN and recurrent neural networks (RNN)-based deep learning ...The classification results and training time of CNN and RNN-based ...
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Combined Features based Spatial Composite Kernel Formation for Hyperspectral Image Classification
... Spectral Image which is taken over northwest Indiana‟s Indian pine test site is ...The classification details with training information are shown in Table ...
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A hyperspectral image classification algorithm based on atrous convolution
... HSI classification. First, the single-pixel classification of HSI learns the whole spectral information of each pixel, which not only solves the prob- lem of large computational complexity of ...
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Robust joint sparsity model for hyperspectral image classification
... DC image was collected by the Hyper- spectral Digital Image Collection Experiment (HYDICE) as shown in ...this image was com- monly used to simulate corrupted data with different kinds of ...The ...
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Hyperspectral image classification via contextual deep learning
... For this data, we randomly pick 9 % labeled samples in each class as training samples and the remainder as test samples. Numbers of training and test sets can be seen in Table 3. SAE-LR uses the first four principal ...
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Hyperspectral image classification with SVM and guided filter
... the classification accuracy for each class, OA, AA, and KA is adopted to evaluate the classification ...the classification maps obtained by dif- ferent methods associated with the corresponding OA ...
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Cone-based joint sparse modelling for hyperspectral image classification
... To sum up, by considering the non-negativity of coefficients for the jointly sparse representation of HSI pixels, a new model called cone-based joint sparse model (J-CSM) has been proposed in this paper. To solve the ...
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An Efficient Objects Discrimination and Noise Reduction On Hyperspectral Images
... Abstract— Hyperspectral imaging (HSI) combines conventional imaging and spectroscopy to attain both spatial and spectral information from an ...a hyperspectral image limits its application and has a ...
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Advanced Spectral and Spatial Techniques for Hyperspectral Image Analysis and Classification
... for hyperspectral image classification, ...the hyperspectral image ...and classification ac- ...of classification accuracy, while in terms of computational time it is ...
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