scholarly journals Local and Global Geometric Structure Preserving and Application to Hyperspectral Image Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Huiwu Luo ◽  
Yuan Yan Tang ◽  
Chunli Li ◽  
Lina Yang
Keyword(s):  
Dimension Reduction ◽  
Geometric Structure ◽  
Hyperspectral Image ◽  
Global Structure ◽  
Hyperspectral Data ◽  
Intrinsic Structure ◽  
Data Set ◽  
Structure Preserving ◽  
Structure Information ◽  
Variance Matrix

Locality Preserving Projection (LPP) has shown great efficiency in feature extraction. LPP captures the locality by theK-nearest neighborhoods. However, recent progress has demonstrated the importance of global geometric structure in discriminant analysis. Thus, both the locality and global geometric structure are critical for dimension reduction. In this paper, a novel linear supervised dimensionality reduction algorithm, calledLocality and Global Geometric Structure Preserving(LGGSP) projection, is proposed for dimension reduction. LGGSP encodes not only the local structure information into the optimal objective functions, but also the global structure information. To be specific, two adjacent matrices, that is, similarity matrix and variance matrix, are constructed to detect the local intrinsic structure. Besides, a margin matrix is defined to capture the global structure of different classes. Finally, the three matrices are integrated into the framework of graph embedding for optimal solution. The proposed scheme is illustrated using both simulated data points and the well-known Indian Pines hyperspectral data set, and the experimental results are promising.

An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Training Data ◽  
Support Vector ◽  
Data Sets ◽  
K Nearest Neighbor ◽  
Data Set ◽  
Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

Dimensionality Reduction and Classification of Hyperspectral Images using Genetic Algorithm

2016 ◽  
Vol 3 (3) ◽  
pp. 503 ◽  
Author(s):  
R. Kiran Kumar ◽  
B. Saichandana ◽  
K. Srinivas
Keyword(s):  
Genetic Algorithm ◽  
Dimensionality Reduction ◽  
Hyperspectral Image ◽  
Image Data ◽  
Unsupervised Classification ◽  
Hyperspectral Data ◽  
Band Selection ◽  
Data Set ◽  
Mode Decomposition ◽  
Fused Image

<p>This paper presents genetic algorithm based band selection and classification on hyperspectral image data set. Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. In this paper, first filtering based on 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, band selection is done using genetic algorithm in-order to remove bands that convey less information. This dimensionality reduction minimizes many requirements such as storage space, computational load, communication bandwidth etc which is imposed on the unsupervised classification algorithms. Next image fusion is performed on the selected hyperspectral bands to selectively merge the maximum possible features from the selected images to form a single image. This fused image is classified using genetic algorithm. Three different indices, such as K-means Index (KMI) and Jm measure are used as objective functions. This method increases classification accuracy and performance of hyperspectral image than without dimensionality reduction.</p>

scholarly journals Hierarchical Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing with Spectral Variability

2020 ◽  
Vol 12 (14) ◽  
pp. 2326 ◽  
Author(s):  
Tatsumi Uezato ◽  
Mathieu Fauvel ◽  
Nicolas Dobigeon
Keyword(s):  
Hyperspectral Image ◽  
Nonnegative Matrix ◽  
Hyperspectral Data ◽  
Spectral Variability ◽  
Data Set ◽  
Sparse Nonnegative Matrix Factorization ◽  
Data Driven Modeling ◽  
Endmember Variability ◽  
Conical Hull ◽  
Unmixing Model

Accounting for endmember variability is a challenging issue when unmixing hyperspectral data. This paper models the variability that is associated with each endmember as a conical hull defined by extremal pixels from the data set. These extremal pixels are considered as so-called prototypal endmember spectra that have meaningful physical interpretation. Capitalizing on this data-driven modeling, the pixels of the hyperspectral image are then described as combinations of these prototypal endmember spectra weighted by bundling coefficients and spatial abundances. The proposed unmixing model not only extracts and clusters the prototypal endmember spectra, but also estimates the abundances of each endmember. The performance of the approach is illustrated thanks to experiments conducted on simulated and real hyperspectral data and it outperforms state-of-the-art methods.

scholarly journals Spectral-Locational-Spatial Manifold Learning for Hyperspectral Images Dimensionality Reduction

2021 ◽  
Vol 13 (14) ◽  
pp. 2752
Author(s):  
Na Li ◽  
Deyun Zhou ◽  
Jiao Shi ◽  
Tao Wu ◽  
Maoguo Gong
Keyword(s):  
Dimensionality Reduction ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Nearest Neighbors ◽  
Cluster Centroid ◽  
Intrinsic Structure ◽  
Adjacency Graph ◽  
Structure Preserving ◽  
Class Labels

Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.

scholarly journals Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3528 ◽  
Author(s):  
Yang Shao ◽  
Jinhui Lan ◽  
Yuzhen Zhang ◽  
Jinlin Zou
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Intrinsic Structure ◽  
Close Link ◽  
Manifold Structure ◽  
Data Space ◽  
Projection Equation

Hyperspectral unmixing, which decomposes mixed pixels into endmembers and corresponding abundance maps of endmembers, has obtained much attention in recent decades. Most spectral unmixing algorithms based on non-negative matrix factorization (NMF) do not explore the intrinsic manifold structure of hyperspectral data space. Studies have proven image data is smooth along the intrinsic manifold structure. Thus, this paper explores the intrinsic manifold structure of hyperspectral data space and introduces manifold learning into NMF for spectral unmixing. Firstly, a novel projection equation is employed to model the intrinsic structure of hyperspectral image preserving spectral information and spatial information of hyperspectral image. Then, a graph regularizer which establishes a close link between hyperspectral image and abundance matrix is introduced in the proposed method to keep intrinsic structure invariant in spectral unmixing. In this way, decomposed abundance matrix is able to preserve the true abundance intrinsic structure, which leads to a more desired spectral unmixing performance. At last, the experimental results including the spectral angle distance and the root mean square error on synthetic and real hyperspectral data prove the superiority of the proposed method over the previous methods.

scholarly journals Hyperspectral dimension reduction using global and local information based linear discriminant analysis

2014 ◽  
Vol II-7 ◽  
pp. 61-66 ◽  
Author(s):  
U. Sakarya
Keyword(s):  
Dimension Reduction ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Global Pattern ◽  
Linear Discriminant ◽  
Local Pattern ◽  
Pattern Information

Hyperspectral image classification has become an important research topic in remote sensing. Because of high dimensional data, a special attention is needed dealing with spectral data; and thus, one of the research topics in hyperspectral image classification is dimension reduction. In this paper, a dimension reduction approach is presented for classification on hyperspectral images. Advantages of the usage of not only global pattern information, but also local pattern information are examined in hyperspectral image processing. In addition, not only tuning the parameters, but also an experimental analysis of the distribution of the hyperspectral data is demonstrated. Therefore, how global or local pattern variations play an important role in classification is examined. According to the experimental outcomes, the promising results are obtained for classification on hyperspectral images.

scholarly journals Dimensionality Reduction using Bi-Dimensional Empirical Mode Decomposition Method for Hyperspectral Image Segmentation

2019 ◽  
Vol 8 (4) ◽  
pp. 11300-11304
Keyword(s):  
Dimensionality Reduction ◽  
Empirical Mode Decomposition ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Clustering Algorithms ◽  
Hyperspectral Data ◽  
Processing Load ◽  
Data Set ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

This paper presents a dimensionality reduction of hyperspectral dataset using bi-dimensional empirical mode decomposition (BEMD). This reduction method is used in a process for segmentation of hyperspectral data. Hyperspectral data contains multiple narrow bands conveying both spectral and spatial information of a scene. Analysis of this kind of data is done in three sequential stages, dimensionality reduction, fusion and segmentation. The method presented in this paper mainly focus on the dimensionality reduction step using BEMD, fusion is carried out using hierarchical fusion method and the segmentation is carried out using Clustering algorithms. This dimensionality reduction removes less informative bands in the data set, decreasing the storage and processing load in further steps in analysis of data. The qualitative and quantitative analysis shows that best informative bands are selected using proposed method which gets high quality segmented image using FCM.

scholarly journals Hyperspectral Image Classification Based on Superpixel Pooling Convolutional Neural Network with Transfer Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 930
Author(s):  
Fuding Xie ◽  
Quanshan Gao ◽  
Cui Jin ◽  
Fengxia Zhao
Keyword(s):  
Neural Network ◽  
Spatial Structure ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Learning Technology ◽  
Structure Information ◽  
Comparative Results

Deep learning-based hyperspectral image (HSI) classification has attracted more and more attention because of its excellent classification ability. Generally, the outstanding performance of these methods mainly depends on a large number of labeled samples. Therefore, it still remains an ongoing challenge how to integrate spatial structure information into these frameworks to classify the HSI with limited training samples. In this study, an effective spectral-spatial HSI classification scheme is proposed based on superpixel pooling convolutional neural network with transfer learning (SP-CNN). The suggested method includes three stages. The first part consists of convolution and pooling operation, which is a down-sampling process to extract the main spectral features of an HSI. The second part is composed of up-sampling and superpixel (homogeneous regions with adaptive shape and size) pooling to explore the spatial structure information of an HSI. Finally, the hyperspectral data with each superpixel as a basic input rather than a pixel are fed to fully connected neural network. In this method, the spectral and spatial information is effectively fused by using superpixel pooling technique. The use of popular transfer learning technology in the proposed classification framework significantly improves the training efficiency of SP-CNN. To evaluate the effectiveness of the SP-CNN, extensive experiments were conducted on three common real HSI datasets acquired from different sensors. With 30 labeled pixels per class, the overall classification accuracy provided by this method on three benchmarks all exceeded 93%, which was at least 4.55% higher than that of several state-of-the-art approaches. Experimental and comparative results prove that the proposed algorithm can effectively classify the HSI with limited training labels.

scholarly journals GRAPH-BASED SEMI-SUPERVISED HYPERSPECTRAL IMAGE CLASSIFICATION USING SPATIAL INFORMATION

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 91-96
Author(s):  
N. Jamshidpour ◽  
S. Homayouni ◽  
A. Safari
Keyword(s):  
Image Classification ◽  
Supervised Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Main Idea ◽  
Hyperspectral Data ◽  
Training Data ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Data Set

Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce.When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

scholarly journals Multilayered autoencoders in problems of hyperspectral image analysis and processing

2021 ◽  
pp. 1-21
Author(s):  
Margarita Georgievna Kuzmina
Keyword(s):  
Image Analysis ◽  
Hyperspectral Image ◽  
Spectral Curve ◽  
Image Data ◽  
Image Features ◽  
Hyperspectral Data ◽  
Spectral Information ◽  
Data Set ◽  
Hyperspectral Image Analysis ◽  
Deep Image

A model of five-layered autoencoder (stacked autoencoder, SAE) is suggested for deep image features extraction and deriving compressed hyperspectral data set specifying the image. Spectral cost function, dependent on spectral curve forms of hyperspectral image, has been used for the autoencoder tuning. At the first step the autoencoder capabilities will be tested based on using pure spectral information contained in image data. The images from well known and widely used hyperspectral databases (Indian Pines, Pavia University и KSC) are planned to be used for the model testing.

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