Spatial distribution preserving-based sparse subspace clustering for hyperspectral image

2019 ◽  
Vol 17 (02) ◽  
pp. 1940010
Author(s):  
Yiyang Ding ◽  
Anyong Qin ◽  
Zhaowei Shang ◽  
Jiye Qian
Keyword(s):  
Spatial Distribution ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Original Data ◽  
Coefficient Matrix ◽  
Intrinsic Structure ◽  
Affine Subspaces ◽  
Novel Approach ◽  
Sparse Subspace Clustering

The high dimensionality and heterogeneity of the hyperspectral image (HSI) make a challenge to the application of machine learning methods, such as sparse subspace clustering (SSC). SSC is designed to represent data as an union of affine subspaces, while it cannot capture the latent structure of the given data. In Mosers theory, the distribution can represent the intrinsic structure efficiently. Hence, we propose a novel approach called spatial distribution preserving-based sparse subspace clustering (SSC-SDP) in this paper for HSI data, which can help sparse representation preserve the underlying manifold structure. Specifically, the density constraint is added by minimizing the inconsistency of the densities estimated in the HSI data and the corresponding sparse coefficient matrix. In addition, we incorporate spatial information into the density estimation of the original data, and the optimization solution based on alternating direction method of multipliers (ADMM) is devised. Three HSI data sets are conducted to evaluate the performance of our SSC-SDP compared with other state-of-art algorithms.

Spectral-Spatial Sparse Subspace Clustering Based on Three-Dimensional Edge-Preserving Filtering for Hyperspectral Image

2019 ◽  
Vol 33 (03) ◽  
pp. 1955003 ◽  
Author(s):  
Ailin Li ◽  
Anyong Qin ◽  
Zhaowei Shang ◽  
Yuan Yan Tang
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Three Dimensional ◽  
Subspace Clustering ◽  
Coefficient Matrix ◽  
Simple Method ◽  
Edge Preserving ◽  
Sparse Coefficient ◽  
Similarity Graph ◽  
Sparse Subspace Clustering

Integrating spatial information into the sparse subspace clustering (SSC) models for hyperspectral images (HSIs) is an effective way to improve clustering accuracy. Since HSI is a three-dimensional (3D) cube datum, 3D spectral-spatial filtering becomes a simple method for extracting the spectral-spatial information. In this paper, a novel spectral-spatial SSC framework based on 3D edge-preserving filtering (EPF) is proposed to improve the clustering accuracy of HSI. First, the initial sparse coefficient matrix is obtained in the sparse representation process of the classical SSC model. Then, a 3D EPF is conducted on the initial sparse coefficient matrix to obtain a more accurate coefficient matrix by solving an optimization problem based on ADMM, which is used to build the similarity graph. Finally, the clustering result of HSI data is achieved by applying the spectral clustering algorithm to the similarity graph. Specifically, the filtered matrix can not only capture the spectral-spatial information but the intensity differences. The experimental results on three real-world HSI datasets demonstrated that the potential of including the proposed 3D EPF into the SSC framework can improve the clustering accuracy.

scholarly journals Sparse Subspace Clustering in Hyperspectral Images using Incomplete Pixels

TecnoLógicas ◽  
2019 ◽  
Vol 22 (46) ◽  
pp. 1-14 ◽  
Author(s):  
Jorge Luis Bacca ◽  
Henry Arguello
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Subspace Clustering ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Image Clustering ◽  
Spectral Signature ◽  
Spectral Image ◽  
Sparse Subspace Clustering

Spectral image clustering is an unsupervised classification method which identifies distributions of pixels using spectral information without requiring a previous training stage. The sparse subspace clustering-based methods (SSC) assume that hyperspectral images lie in the union of multiple low-dimensional subspaces.  Using this, SSC groups spectral signatures in different subspaces, expressing each spectral signature as a sparse linear combination of all pixels, ensuring that the non-zero elements belong to the same class. Although these methods have shown good accuracy for unsupervised classification of hyperspectral images, the computational complexity becomes intractable as the number of pixels increases, i.e. when the spatial dimension of the image is large. For this reason, this paper proposes to reduce the number of pixels to be classified in the hyperspectral image, and later, the clustering results for the missing pixels are obtained by exploiting the spatial information. Specifically, this work proposes two methodologies to remove the pixels, the first one is based on spatial blue noise distribution which reduces the probability to remove cluster of neighboring pixels, and the second is a sub-sampling procedure that eliminates every two contiguous pixels, preserving the spatial structure of the scene. The performance of the proposed spectral image clustering framework is evaluated in three datasets showing that a similar accuracy is obtained when up to 50% of the pixels are removed, in addition, it is up to 7.9 times faster compared to the classification of the data sets without incomplete pixels.

scholarly journals HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 945-948 ◽  
Author(s):  
Hongyan Zhang ◽  
Han Zhai ◽  
Wenzhi Liao ◽  
Liqin Cao ◽  
Liangpei Zhang ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Dimensional Space ◽  
Contextual Information ◽  
Subspace Clustering ◽  
Coefficient Matrix ◽  
Clustering Methods ◽  
Max Pooling ◽  
Higher Dimensional ◽  
Sparse Subspace Clustering ◽  
Nonlinear Manifolds

In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.

scholarly journals HYPERSPECTRAL IMAGE KERNEL SPARSE SUBSPACE CLUSTERING WITH SPATIAL MAX POOLING OPERATION

2016 ◽  
Vol XLI-B3 ◽  
pp. 945-948 ◽  
Author(s):  
Hongyan Zhang ◽  
Han Zhai ◽  
Wenzhi Liao ◽  
Liqin Cao ◽  
Liangpei Zhang ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Dimensional Space ◽  
Contextual Information ◽  
Subspace Clustering ◽  
Coefficient Matrix ◽  
Clustering Methods ◽  
Max Pooling ◽  
Higher Dimensional ◽  
Sparse Subspace Clustering ◽  
Nonlinear Manifolds

In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore the complex nonlinear structure of hyperspectral images (HSIs) and obtain a much more accurate representation coefficient matrix. Secondly, through the spatial max pooling operation, the spatial contextual information is integrated to obtain a smoother clustering result. Through experiments, it is verified that the KSSC-SMP algorithm is a competitive clustering method for HSIs and outperforms the state-of-the-art clustering methods.

scholarly journals A fast and accurate similarity-constrained subspace clustering algorithm for land cover segmentation

2021 ◽  
Author(s):  
Carlos Hinojosa ◽  
Esteban Vera ◽  
Henry Arguello
Keyword(s):  
Land Cover ◽  
High Performance ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Subspace Clustering ◽  
Coefficient Matrix ◽  
Clustering Methods ◽  
Spatial Homogeneity ◽  
Discrimination Capability ◽  
Unsupervised Deep Learning

Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made for developing a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as Sparse Subspace Clustering (SSC), have become a popular tool for unsupervised learning due to their high performance. However, the computational complexity of SSC methods prevents their use on large spectral remotely sensed datasets. Furthermore, since SSC ignores the spatial information in the spectral images, its discrimination capability is limited, hampering the clustering results' spatial homogeneity. To address these two relevant issues, in this paper, we propose a fast algorithm that obtains a sparse representation coefficient matrix by first selecting a small set of pixels that best represent their neighborhood. Then, it performs spatial filtering to enforce the connectivity of neighboring pixels and uses fast spectral clustering to get the final segmentation. Extensive simulations with our method demonstrate its effectiveness in land cover segmentation, obtaining remarkable high clustering performance compared with state-of-the-art SSC-based algorithms and even novel unsupervised-deep-learning-based methods. Besides, the proposed method is up to three orders of magnitude faster than SSC when clustering more than 2x10<sup>4</sup> spectral pixels.

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.

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