Framework of a supervised hyperspectral image classification system . 98
Synergetics Framework for Hyperspectral Image Classification
... the classification accuracy on the data ...a classification with the same training data set using well-known distance measures and classification techniques widely used in hyperspectral data ...
6
A classwise supervised ordering approach for morphology based hyperspectral image classification
... spectral-spatial classification of hyperspectral images, by means of morphological features and manifold ...to hyperspectral data is problematic, due to the absence of a complete lattice structure at ...
5
Hyperspectral Image Classification
... classifiers. Supervised approaches classify input data for each class using a set of representative samples known as training ...samples. Hyperspectral (HS) image classification always suffers ...
26
A hypergraph based semi-supervised band selection method for hyperspectral image classification
... in hyperspectral data ...tral image samples. Secondly, the semi-supervised learning method not only utilizes unlabeled samples in the learning process to improve model performance, but also requires ...
5
Hyperspectral Image Classification for Remote Sensing
... a supervised dimensionality reduc- tion (DR) technique where labels of the samples are used during the DR ...sensing hyperspectral datasets is the lack of enough labeled data, we proposed a method to ...
142
Extreme sparse multinomial logistic regression : a fast and robust framework for hyperspectral image classification
... ESMLR framework to solve the two main drawbacks SMLR for the effective classification of the ...the classification accuracy has been successfully ...ESMLR framework has yielded better ...
22
Multiscale spatial-spectral convolutional network with image-based framework for hyperspectral imagery classification
... patch-based classification method will consume more time when processing a larger ...the image-based classification and the processing of testing phase is faster than the patch-based ...
21
Extreme sparse multinomial logistic regression : a fast and robust framework for hyperspectral image classification
... ESMLR framework to solve the two main drawbacks SMLR for the effective classification of the ...the classification accuracy has been successfully ...ESMLR framework has yielded better ...
23
Decision fusion framework for hyperspectral image classification based on Markov and conditional random fields
... fusion framework addressing these ...proposed framework offers flexibility such that it can be extended with additional decision sources in a straightforward ...real hyperspectral images show ...
20
Hyperspectral Image Segmentation and Classification using FODPSO
... NTRODUCTION Hyperspectral imaging systems have gained a great attention from researchers in the past few ...by hyperspectral sensors, the possibility of accurately discriminating materials of interest with ...
6
Generative Adversarial Networks for Hyperspectral Image Classification
... perform supervised dimensionality reduction, by projecting the input data to a linear subspace consisting of the directions which maximize the separation between ...
18
Hyperspectral image classification with SVM and guided filter
... 2 Related methodology and work 2.1 SVM and HSI classification SVM is a supervised machine learning method, proposed by Vapnik [19], which is based on the statistical learning theory. Essentially, SVM ...
9
Tensor morphological profile for hyperspectral image classification
... tral image classification. TMP is a general framework to extract the multi-dimensional structures in high-dimensional ...treating hyperspectral images a tensor, it is possible to extend the ...
5
Flower Grain Image Classification Using Supervised Classification Algorithm
... computer system is developed the model to identify pollen grain and classify ...proposed system identifies flower pollen grain images and classifies them into different ...proposed system we used ...
6
Spectral-spatial Feature Extraction for Hyperspectral Image Classification
... the classification accuracy with overlapped testing samples has remarkable improve- ment when a larger filter size is ...the classification results, they are not desired because they violate the basic ...
179
Sparse representation based hyperspectral image compression and classification
... hand, hyperspectral image compression presents its own challenges and demands methods beyond the existing data compression approaches designed for regular ...lossy hyperspectral image ...
143
Segmented Mixture-of-Gaussian Classification for Hyperspectral Image Analysis
... Mixture-of-Gaussian Classification for Hyperspectral Image Analysis Saurabh Prasad, Member, IEEE, Minshan Cui, Wei Li, and James ...of hyperspectral imagery that facilitates detection of ...
5
Unsupervised Classifier Selection Approach for Hyperspectral Image Classification
... the framework of multiple classifier system for HSI ...energy framework to select a subset of reasonably accurate classifiers for the mul- tiple classifier ...
5
Essays on hyperspectral image analysis: classification and target detection
... HSI classification but from only a limited number of labelled training samples, it is a promising direction to utilise the structure information as much as ...HSI classification made by the JSM-based ...
209
Deep Recurrent Neural Networks for Hyperspectral Image Classification
... the supervised models for hyperspectral images described earlier are vector-based ...representing hyperspectral pixels, which intrinsically have a sequence-based data ...CNN-based ...
17