[PDF] Top 20 Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns
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Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns
... in spectral-spatial features classification for hyperspectral images (HSI) with high spatial ...novel Spectral-spatial classification method for improving ... See full document
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Spectral-spatial classification of hyperspectral data using spectral-domain local binary patterns
... in spectral-spatial features classification for hyperspectral images (HSI) with high spatial ...novel Spectral-spatial classification method for improving ... See full document
23
Spectral-Spatial Classification Integrating Band Selection for Hyperspectral Imagery With Severe Noise Bands
... Abstract—Spectral-spatial classification for hyperspectral im- agery has been receiving much attention, since the detailed spectral and rich spatial information of ... See full document
13
Frontiers in Spectral-Spatial Classification of Hyperspectral Images
... spaceborne hyperspectral imaging systems have advanced in recent years in terms of spectral and spatial resolution, which makes data sets produced by them a valuable source for land-cover ... See full document
32
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification Using Markov Random Fields
... training data and validating over the remain- ing ...while using it with the ...so binary classifiers were trained in one-vs-one setup and the multi-class probabilities were estimated using ... See full document
6
Texture Classification using Local Binary Patterns and Modular PCA
... Local Binary Pattern is a powerful feature extractor tool and the main advantage of using PCA [21] is data compression, by reducing the number of ...8, spatial resolution (R) as 1 and ... See full document
6
Hybrid Subspace Detection based on Spectral and Spatial Information for Effective Hyperspectral Image Classification
... in data inspection [2, 3]. This high dimensional data provides many research chanllenges as it requires more processing ...the classification accuracy increases with increase of the no of features ... See full document
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Advanced Spectral and Spatial Techniques for Hyperspectral Image Analysis and Classification
... tree data representation is able to increase the filtering ...given binary predicate T, and removing the ...(i.e., local maxima and minima in the image) until reaching only a single component fully ... See full document
172
Group sparse representation based on nonlocal spatial and local spectral similarity for hyperspectral imagery classification
... evaluated using three widely used hyperspectral data ...with spectral coverage ranging from ...The spatial resolution of this image is ...224 spectral bands ranging from ...with ... See full document
19
On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification
... the spectral- spatial methods, it is necessary to develop a new sampling strategy to separate the training and testing sets without ...of data overlap and make the evaluation fair ...the ... See full document
15
A Visualization Method for Isosurface of Hyperspectral Data Combining the Spatial and Spectral Dimensions
... assisting data analysis and so ...of hyperspectral data and analyze the characteristics of them ...for spatial and spectral dimensions of hyperspectral data is studied by ... See full document
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Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling
... For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random ... See full document
12
Spatial-spectral classification of hyperspectral images : a deep learning framework with Markov random fields based modeling
... For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random ... See full document
13
Remote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding (CSSLE)
... sensing data analysis using combined spectral and spatial linear embedding ...combined spectral and spatial linear embedding is promising for remote sensing data feature ... See full document
6
Learning to Pay Attention on Spectral Domain: A Spectral Attention Module-Based Convolutional Network for Hyperspectral Image Classification
... years, hyperspectral image classification using convolutional neural networks (CNNs) has progressed ...that hyperspectral images are of high dimensionality, CNNs can be hindered by their ... See full document
13
Spectral non-local restoration of hyperspectral images with low-rank property
... or classification. In this paper, we propose a new low-rank spectral nonlocal approach (LRSNL) to the simul- taneous removal of a mixture of different types of noises, such as Gaussian noises, salt and ... See full document
7
Unsupervised Spectral-Spatial Feature Learning via Deep Residual Conv-Deconv Network for Hyperspectral Image Classification
... the hyperspectral image and own good description power for semantic visual patterns in the object ...truth data, object detection achieved by this method is free and totally ...unsupervised ... See full document
16
Linear vs. nonlinear extreme learning machine for spectral-spatial classification of hyperspectral images
... the classification accuracy is not very high when applied to hyperspectral ...better classification results than LELM. The classification accuracy of KELM is improved but still not high enough ... See full document
15
A multiscale modified minimum spanning forest method for spatial-spectral hyperspectral images classification
... hyper- spectral images, a vector with several hundreds of spec- trums is assigned to each spatial ...precious spectral information enhances the capability of recognizing physical material and other ... See full document
12
Optimal structural and spectral features for tree species classification using combined airborne laser scanning and hyperspectral data
... species classification based on multi source earth observation data was ...(ALS) data, as well as their ...selected spectral features are more distributed across the spectrum, in contrast to ... See full document
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