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DIMENSIONALITY REDUCTION OF HYPERSPECTRAL IMAGES BY COMBINATION OF NON-PARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE) AND MODIFIED NEIGHBORHOOD PRESERVING EMBEDDING (NPE)

T. Alipour Fard, H. Arefi
2014 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Experimental results on well-known hyperspectral dataset demonstrate that compared to conventional extraction algorithms the overall accuracy of the classification increased.  ...  This paper combine two conventional feature extraction methods (NWFE&NPE) in a novel framework and present a new semi-supervised feature extraction method called Adjusted Semi supervised Discriminant Analysis  ...  Liao, W., Pizurica, A., Philips, W., Pi, Y., 2011, Feature extraction for hyperspectral images based on semi-supervised local discriminant analysis, JURSE 2011, Munich, Germany.  ... 
doi:10.5194/isprsarchives-xl-2-w3-31-2014 fatcat:mcsx75w47bhm7o5i2gvkd2xjoe

Tensor Representation and Manifold Learning Methods for Remote Sensing Images [article]

Lefei Zhang
2014 arXiv   pre-print
This thesis targets to develop some efficient information extraction algorithms for RS images, by relying on the advanced technologies in machine learning.  ...  to manually interpret these images.  ...  In this paper, we introduce a modified stochastic neighbor embedding (MSNE) algorithm for multiple features dimension reduction (DR) under a probability preserving projection framework.  ... 
arXiv:1401.2871v1 fatcat:7riwgc3pc5hcpm3iczsy2tsali

Segmented Mixture-of-Gaussian Classification for Hyperspectral Image Analysis

Saurabh Prasad, Minshan Cui, Wei Li, James E. Fowler
2014 IEEE Geoscience and Remote Sensing Letters  
Traditional approaches to addressing this issue, which typically employ dimensionality reduction based on either projection or feature selection, are at best suboptimal for hyperspectral classification  ...  The locality-preserving discriminant analysis preserves the potentially multimodal statistical structure of the data, which the Gaussian mixture model classifier learns in the reduced-dimensional subspace  ...  In [1] , we studied a new approach for HSI classification based on a locality-preserving dimensionality-reduction step-local Fisher's discriminant analysis (LFDA)-as well as a Gaussianmixture-model (GMM  ... 
doi:10.1109/lgrs.2013.2250902 fatcat:riqe626wcjbw7a5rtu5mi2nqa4

Spatial Context based Angular Information Preserving Projection for Hyperspectral Image Classification [article]

Minshan Cui, Saurabh Prasad
2016 arXiv   pre-print
Dimensionality reduction is a crucial preprocessing for hyperspectral data analysis - finding an appropriate subspace is often required for subsequent image classification.  ...  Since unlabeled data are often more readily available compared to labeled data, we propose an unsupervised projection that finds a lower dimensional subspace where local angular information is preserved  ...  We first form a unsupervised version of ADA which we refer to as local similarity preserving projection (LSPP).  ... 
arXiv:1607.04593v1 fatcat:vshnyg2tdfbjxmyr66gpwgpi74

Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification

Wei Li, Saurabh Prasad, James E. Fowler, Lori Mann Bruce
2011 IEEE Geoscience and Remote Sensing Letters  
In this letter, a modified KDA algorithm, i.e., kernel local Fisher discriminant analysis (KLFDA), is studied for HSI analysis.  ...  Linear discriminant analysis (LDA) has been widely applied for hyperspectral image (HSI) analysis as a popular method for feature extraction and dimensionality reduction.  ...  for hyperspectral classification.  ... 
doi:10.1109/lgrs.2011.2128854 fatcat:7fxpyt5wljfqljizahwhccxota

Unsupervised spectral sub-feature learning for hyperspectral image classification

Viktor Slavkovikj, Steven Verstockt, Wesley De Neve, Sofie Van Hoecke, Rik Van de Walle
2016 International Journal of Remote Sensing  
In this article, we propose an unsupervised feature learning method for classification of hyperspectral images.  ...  Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis.  ...  Similar dimensionality reduction methods, such as neighbourhood-preserving embedding (NPE) (He et al. 2005) , locality-preserving projection (LPP) (He and Niyogi 2004) , and linear local tangent space  ... 
doi:10.1080/01431161.2015.1125554 fatcat:ef6tvu4qi5c3vnbqnygpe3rg6a

Hyperspectral tree species classification with an aid of lidar data

Tomohiro Matsuki, Naoto Yokoya, Akira Iwasaki
2014 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)  
Classification of tree species is one of the most important applications in remote sensing. A methodology to classify tree species using hyperspectral and LiDAR data is proposed.  ...  As a result, the authors achieved classification accuracy of 79 % with 10 % training data, which is 17 % higher than what is obtained by using hyperspectral data only.  ...  Therefore, shadows in hyperspectral data need to be modified for accurate classification. We used the unmixing-based approach for de-shadowing of reflectance data [2] .  ... 
doi:10.1109/whispers.2014.8077510 dblp:conf/whispers/MatsukiYI14 fatcat:qxxxcvrrcbedbcoz4seezp7woi

Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images

Wenzhi Liao, A. Pizurica, P. Scheunders, W. Philips, Youguo Pi
2013 IEEE Transactions on Geoscience and Remote Sensing  
We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance in both illposed and poor-posed conditions  ...  The underlying idea is to design an optimal projection matrix, which preserves the local neighborhood information inferred from unlabeled samples, while simultaneously maximizing the class discrimination  ...  Landgrebe for providing the AVIRIS Indian Pines and Washington DC Mall data sets, Prof. Crawford for providing KSC and Botswana data sets, Prof. Cai for providing SDA source code, Prof.  ... 
doi:10.1109/tgrs.2012.2200106 fatcat:66cp43n5ofebppkmif3tijde74

A Discontinuity Preserving Relaxation Scheme for Spectral–Spatial Hyperspectral Image Classification

Jun Li, Mahdi Khodadadzadeh, Antonio Plaza, Xiuping Jia, Jose M. Bioucas-Dias
2016 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this work, we develop a discontinuity preserving relaxation strategy, which can be used for postprocessing of class probability estimates, as well as preprocessing of the original hyperspectral image  ...  On the other hand, relaxation (as a postprocessing approach) works on the label image or class probabilities obtained from pixelwise classifiers.  ...  The authors would like to thank the Editors and the Anonymous Reviewers for their detailed and highly constructive comments, which greatly helped us to improve the technical quality and presentation of  ... 
doi:10.1109/jstars.2015.2470129 fatcat:s5aoj6kj5jezng4zkcvhvbdywu

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Tatireddy Reddy, Jonnadula Harikiran
2022 Journal of Spectral Imaging  
Hyperspectral imaging is used in a wide range of applications.  ...  Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.  ...  In this approach, initially, the 3DCNN on the local image patch was employed to obtain the local spatial-spectral characteristics.  ... 
doi:10.1255/jsi.2022.a1 fatcat:rue5klkmlfcrzftepc6lzfcbfe

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Pedram Ghamisi, Naoto Yokoya, Jun Li, Wenzhi Liao, Sicong Liu, Javier Plaza, Behnood Rasti, Antonio Plaza
2017 IEEE Geoscience and Remote Sensing Magazine  
Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide.  ...  For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and  ...  In addition, the authors would like to thank the National Center for Airborne Laser Mapping (NCALM) at the University of Houston for providing the CASI Houston data set, and the IEEE GRSS Image Analysis  ... 
doi:10.1109/mgrs.2017.2762087 fatcat:6ezzye7yyvacbouduqv2f2c7gi

Application of unsupervised nearest-neighbor density-based approaches to sequential dimensionality reduction and clustering of hyperspectral images

Claude Cariou, Kacem Chehdi, Lorenzo Bruzzone, Francesca Bovolo, Jon Atli Benediktsson
2018 Image and Signal Processing for Remote Sensing XXIV  
ABSTRACT In this communication, we address the problem of unsupervised dimensionality reduction (DR) for hyperspectral images (HSIs), using nearest-neighbor density-based (NN-DB) approaches.  ...  Application of unsupervised nearest-neighbor density-based approaches to sequential dimensionality reduction and clustering of hyperspectral images.  ...  This is why it can be useful to modify the NN-DB methods to avoid merging spectrally distant bands into the same band cluster, therefore preserving the physical nature of hyperspectral image formation.  ... 
doi:10.1117/12.2325530 fatcat:2tpq5qaya5e3nbh74iezjtoiba

A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images

Ramanarayan Mohanty, S. L. Happy, Aurobinda Routray
2018 IEEE Geoscience and Remote Sensing Letters  
The lack of proper class discrimination among the Hyperspectral (HS) data points poses a potential challenge in HS classification.  ...  To address this issue, this paper proposes an optimal geometry-aware transformation for enhancing the classification accuracy.  ...  These local approaches use spectral embedding method to retain the local geometry of the data while projecting them to lower dimensions.  ... 
doi:10.1109/lgrs.2018.2804888 fatcat:ig2gd6bnhndpjoefenh6mfeivi

Visualization of Hyperspectral Images Using Bilateral Filtering

Ketan Kotwal, Subhasis Chaudhuri
2010 IEEE Transactions on Geoscience and Remote Sensing  
This paper presents a new approach for hyperspectral image visualization. A bilateral filtering-based approach is presented for hyperspectral image fusion to generate an appropriate resultant image.  ...  The proposed approach retains even the minor details that exist in individual image bands, by exploiting the edge-preserving characteristics of a bilateral filter.  ...  ACKNOWLEDGMENT The authors would like to thank the reviewers for their constructive suggestions and the Bharti Centre for Communication for the logistic support.  ... 
doi:10.1109/tgrs.2009.2037950 fatcat:nwn5nwgl3vdadjs4siow37dzfe

SPCA Assisted Correlation Clustering of Hyperspectral Imagery

A. Mehta, O. Dikshit
2014 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this study, correlation clustering is introduced to hyperspectral imagery for unsupervised classification.  ...  Experiments are conducted on three real hyperspectral images. Preliminary analysis of algorithms on real hyperspectral imagery shows ORCLUS is able to produce acceptable results.  ...  David A. Landgrebe of Purdue University and Prof. Paolo Gamba of University of Pavia for making available the hyperspectral imagery used in this study.  ... 
doi:10.5194/isprsannals-ii-8-111-2014 fatcat:x5gm2pvz3rakbkmaeo2e7ahkn4
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