Swarnajyoti Patra

Vis. Comput., February, 2024

Extended Semantic Edge-Aware Filtering Profile for Hyperspectral Image Classification.

[BibT_eX]

[DOI]

Kunal Pradhan

IEEE Geosci. Remote. Sens. Lett., 2024

2023

Active Learning for Hyperspectral Image Classification Using Kernel Sparse Representation Classifiers.

[BibT_eX]

[DOI]

Amos Bortiew

IEEE Geosci. Remote. Sens. Lett., 2023

2021

Deep convolution neural network with automatic attribute profiles for hyperspectral image classification.

[BibT_eX]

[DOI]

Multim. Tools Appl., 2021

A novel dependency definition exploiting boundary samples in rough set theory for hyperspectral band selection.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2021

2020

A Novel Threshold Detection Technique for the Automatic Construction of Attribute Profiles in Hyperspectral Images.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2020

A rough-GA based optimal feature selection in attribute profiles for classification of hyperspectral imagery.

[BibT_eX]

[DOI]

Soft Comput., 2020

Variable precision rough set based unsupervised band selection technique for hyperspectral image classification.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2020

2019

Threshold-Free Attribute Profile for Classification of Hyperspectral Images.

[BibT_eX]

[DOI]

IEEE Trans. Geosci. Remote. Sens., 2019

A novel technique to detect a suboptimal threshold of neighborhood rough sets for hyperspectral band selection.

[BibT_eX]

[DOI]

Soft Comput., 2019

Empirical study of neighbourhood rough sets based band selection techniques for classification of hyperspectral images.

[BibT_eX]

[DOI]

IET Image Process., 2019

Automatic Attribute Profiles for Spectral-Spatial Classification of Hyperspectral Images.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition and Machine Intelligence, 2019

2018

A fast partition-based batch-mode active learning technique using SVM classifier.

[BibT_eX]

[DOI]

Soft Comput., 2018

Morphological complexity profile for the analysis of hyperspectral images.

[BibT_eX]

[DOI]

Proceedings of the 2018 4th International Conference on Recent Advances in Information Technology (RAIT), 2018

2017

A Spectral-Spatial Multicriteria Active Learning Technique for Hyperspectral Image Classification.

[BibT_eX]

[DOI]

IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2017

A fast automatic optimal threshold selection technique for image segmentation.

[BibT_eX]

[DOI]

Signal Image Video Process., 2017

2016

PSO Based Context Sensitive Thresholding Technique for Automatic Image Segmentation.

[BibT_eX]

[DOI]

Proceedings of Sixth International Conference on Soft Computing for Problem Solving, 2016

2015

Hyperspectral Band Selection Based on Rough Set.

[BibT_eX]

[DOI]

Prahlad Modi

IEEE Trans. Geosci. Remote. Sens., 2015

A rough set based band selection technique for the analysis of hyperspectral images.

[BibT_eX]

[DOI]

Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium, 2015

2014

A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification.

[BibT_eX]

[DOI]

IEEE Trans. Geosci. Remote. Sens., 2014

A novel classification technique based on progressive transductive SVM learning.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2014

A novel context sensitive multilevel thresholding for image segmentation.

[BibT_eX]

[DOI]

Rahul Gautam

Appl. Soft Comput., 2014

2012

A cluster-assumption based batch mode active learning technique.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2012

A Batch-Mode Active Learning Technique Based on Multiple Uncertainty for SVM Classifier.

[BibT_eX]

[DOI]

IEEE Geosci. Remote. Sens. Lett., 2012

A novel SOM-based active learning technique for classification of remote sensing images with SVM.

[BibT_eX]

[DOI]

Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012

2011

A Fast Cluster-Assumption Based Active-Learning Technique for Classification of Remote Sensing Images.

[BibT_eX]

[DOI]

IEEE Trans. Geosci. Remote. Sens., 2011

2009

An unsupervised context-sensitive change detection technique based on modified self-organizing feature map neural network.

[BibT_eX]

[DOI]

Int. J. Approx. Reason., 2009

2008

A Neural Approach to Unsupervised Change Detection of Remote-Sensing Images.

[BibT_eX]

[DOI]

Proceedings of the Speech, 2008

Change Detection of Remote Sensing Images with Semi-supervised Multilayer Perceptron.

[BibT_eX]

[DOI]

Fundam. Informaticae, 2008

2007

A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks.

[BibT_eX]

[DOI]

IEEE Trans. Geosci. Remote. Sens., 2007

Semi-supervised Learning with Multilayer Perceptron for Detecting Changes of Remote Sensing Images.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition and Machine Intelligence, 2007

Unsupervised Change Detection in Remote-Sensing Images Using Modified Self-Organizing Feature Map Neural Network.

[BibT_eX]

[DOI]

Proceedings of the 2007 International Conference on Computing: Theory and Applications (ICCTA 2007), 2007

2006

Unsupervised Change Detection in Remote-Sensing Images using One-dimensional Self-Organizing Feature Map Neural Network.

[BibT_eX]

[DOI]