[PDF] Top 20 Cone-based joint sparse modelling for hyperspectral image classification

... a joint sparse model (JSM) has been successfully developed for HSI classification and has achieved promising performance ...by sparse linear combinations of a few common training samples, ... See full document

... Here, sparse noise is defined as the noise of arbitrary magnitude that only affects certain bands or ...the classification performance, we are not aware of any classification method that takes it ex- ... See full document

... of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for ...that ... See full document

... the image and the surrounding neighbors help identify their ...a joint spectral-spatial feature extraction method that assigns different weights to the neighboring pixels depending on how far they are from ... See full document

... hypothesis modelling, researches have ex- plored a variety of techniques and extensions on the basis of ...hand, sparse rep- resentation (SR) techniques [10] originating from compressed sensing have been ... See full document

... The sparse representation classification (SRC)-based method has been found to be a powerful tool for numerous computer vision ...HSI classification [24]. Assume that one test pixel in the HSI ... See full document

... An image pyramid refers to an image that is subject to repeated smoothing and subsampling and generates a series of weighted down images ...Gaussian image pyramid was used to generate images with ... See full document

... Abstract: Classification of hyperspectral images is a challenging task owing to the high dimensionality of the data, limited ground truth data, collinearity of the spectra and the presence of mixed ... See full document

... and sparse coding. The main difference is that SDL uses the sparse coefficients as the feature vectors for SVM, while CSCSVM incorporates the contextual information with the sparse coefficients ... See full document

... (LVQ)- based dictionary learning method ...in sparse coding can be lessened if simultaneous sparse approximation is ...a joint or Laplacian sparsity prior and the TDDL framework ... See full document

... Fuzzy Logic makes conversion of imprecise information to precise one, consists of capability to design rational decisions containing imperfect information. Uncertainty, imprecision, incompleteness, risk management, ... See full document

... the hyperspectral face recognition by extracting spectral features picked from typical face regions, such as forehead, cheeks, hair, and ...on hyperspectral faces and transformed high dimension faces into ... See full document

... regions. Hyperspectral Image (HSI) and multispectral image play a vital role in the field of remote ...the classification of pixels in an image into a number of ...of image ... See full document

... Pixel-wise classification methods process each pixel independently without considering the spatial informa- tion, but spatial contextual information of HSI is as impor- tant as the spectral information ...of ... See full document

... It is clear that the classification for the individual class is coherent, i.e., most of the pixels belonging to a specific class are distributed into the same class. The forest, residential area and farmlands can ... See full document

... feature-based classification (SSFC) framework in which a balanced local discriminant embedding algorithm was adopted to reduce the dimension, a CNN was introduced to explore the high-level features and a ... See full document

... Spectral Image which is taken over northwest Indiana‟s Indian pine test site is ...The classification details with training information are shown in Table ... See full document

... HSI classification is a significant challenge in remote sensing ...HIS classification algorithms fall into three categories: supervised, unsupervised, and ...the hyperspectral images, the supervised ... See full document

... HRS image scenes ...and sparse structure are important but are quite different in ...multi-task joint sparse and low -rank representation is a simple yet effective way to fuse multiple ... See full document

... for hyperspectral images described earlier are vector-based ...representing hyperspectral pixels, which intrinsically have a sequence-based data ...CNN-based classification, are ... See full document