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DIMENSIONALITY REDUCTION OF HYPERSPECTRAL IMAGES BY COMBINATION OF NON-PARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE) AND MODIFIED NEIGHBORHOOD PRESERVING EMBEDDING (NPE)
2014
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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]
2014
arXiv
pre-print
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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
2014
IEEE Geoscience and Remote Sensing Letters
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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]
2016
arXiv
pre-print
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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
2011
IEEE Geoscience and Remote Sensing Letters
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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
2016
International Journal of Remote Sensing
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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
2014
2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
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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
2013
IEEE Transactions on Geoscience and Remote Sensing
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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
2016
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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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
2022
Journal of Spectral Imaging
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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
2017
IEEE Geoscience and Remote Sensing Magazine
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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
2018
Image and Signal Processing for Remote Sensing XXIV
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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
2018
IEEE Geoscience and Remote Sensing Letters
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2018 pre-print arXiv:1807.02682v1 fatcat:6roe2ks57verrphxtfqomo7c2i
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
2010
IEEE Transactions on Geoscience and Remote Sensing
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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
2014
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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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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