scholarly journals A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing

2019 ◽  
Vol 11 (19) ◽  
pp. 2188
Author(s):  
Li ◽  
Zhu ◽  
Guo ◽  
Chen
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Spatial Information ◽  
Mixing Model ◽  
Spectral Unmixing ◽  
Hyperspectral Images ◽  
Hyperspectral Unmixing ◽  
Manifold Structure ◽  
Underlying Manifold

Spectral unmixing of hyperspectral images is an important issue in the fields of remotesensing. Jointly exploring the spectral and spatial information embedded in the data is helpful toenhance the consistency between mixing/unmixing models and real scenarios. This paper proposesa graph regularized nonlinear unmixing method based on the recent multilinear mixing model(MLM). The MLM takes account of all orders of interactions between endmembers, and indicates thepixel-wise nonlinearity with a single probability parameter. By incorporating the Laplacian graphregularizers, the proposed method exploits the underlying manifold structure of the pixels’ spectra,in order to augment the estimations of both abundances and nonlinear probability parameters.Besides the spectrum-based regularizations, the sparsity of abundances is also incorporated for theproposed model. The resulting optimization problem is addressed by using the alternating directionmethod of multipliers (ADMM), yielding the so-called graph regularized MLM (G-MLM) algorithm.To implement the proposed method on large hypersepectral images in real world, we proposeto utilize a superpixel construction approach before unmixing, and then apply G-MLM on eachsuperpixel. The proposed methods achieve superior unmixing performances to state-of-the-artstrategies in terms of both abundances and probability parameters, on both synthetic and real datasets.

scholarly journals Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing

2021 ◽  
Vol 13 (16) ◽  
pp. 3147
Author(s):  
Ziqiang Hua ◽  
Xiaorun Li ◽  
Jianfeng Jiang ◽  
Liaoying Zhao
Keyword(s):  
Real World ◽  
Spatial Information ◽  
State Of The Art ◽  
The State ◽  
Spectral Unmixing ◽  
Experimental Results ◽  
Spectral Information ◽  
Hyperspectral Unmixing ◽  
Spatial Features ◽  
Gating Mechanism

Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing.

scholarly journals Approximate Sparse Regularized Hyperspectral Unmixing

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Chengzhi Deng ◽  
Yaning Zhang ◽  
Shengqian Wang ◽  
Shaoquan Zhang ◽  
Wei Tian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Optimization Model ◽  
Optimization Problem ◽  
Augmented Lagrangian ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Hyperspectral Unmixing ◽  
Variable Splitting

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser thanl1regularizer and much easier to be solved thanl0regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison tol1regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-artl1regularizer.

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 Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

2021 ◽  
Vol 13 (2) ◽  
pp. 268
Author(s):  
Xiaochen Lv ◽  
Wenhong Wang ◽  
Hongfu Liu
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Imbalanced Data ◽  
Reconstruction Error ◽  
Hyperspectral Data ◽  
Weight Matrix ◽  
Hyperspectral Images ◽  
Mixed Data ◽  
Sparsity Constraints ◽  
Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

scholarly journals DLR HySU—A Benchmark Dataset for Spectral Unmixing

2021 ◽  
Vol 13 (13) ◽  
pp. 2559
Author(s):  
Daniele Cerra ◽  
Miguel Pato ◽  
Kevin Alonso ◽  
Claas Köhler ◽  
Mathias Schneider ◽  
...  
Keyword(s):  
Spectral Unmixing ◽  
Benchmark Dataset ◽  
Data Sets ◽  
Endmember Extraction ◽  
Imaging Spectrometer ◽  
Hyperspectral Unmixing ◽  
Processing Step ◽  
German Aerospace ◽  
Spectrometer Data ◽  
First Time

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

scholarly journals Correction: Zhang, G., et al. Shadow Detection and Restoration for Hyperspectral Images Based on Nonlinear Spectral Unmixing. Remote Sensing 2020, 12, 3985

2021 ◽  
Vol 13 (3) ◽  
pp. 473
Author(s):  
Guichen Zhang ◽  
Daniele Cerra ◽  
Rupert Müller
Keyword(s):  
Remote Sensing ◽  
Spectral Unmixing ◽  
Shadow Detection ◽  
Hyperspectral Images

The authors would like to make the following correction of [...]

Using spatial information for evaluating the quality of prediction maps from hyperspectral images: A geostatistical approach

2019 ◽  
Vol 1077 ◽  
pp. 116-128 ◽  
Author(s):  
Ana Herrero-Langreo ◽  
Nathalie Gorretta ◽  
Bruno Tisseyre ◽  
Aoife Gowen ◽  
Jun-Li Xu ◽  
...  
Keyword(s):  
Spatial Information ◽  
Hyperspectral Images ◽  
Geostatistical Approach

scholarly journals Nonlocal Estimation of Manifold Structure

2006 ◽  
Vol 18 (10) ◽  
pp. 2509-2528 ◽  
Author(s):  
Yoshua Bengio ◽  
Martin Monperrus ◽  
Hugo Larochelle
Keyword(s):  
Large Class ◽  
Manifold Learning ◽  
Learning Algorithms ◽  
Tangent Plane ◽  
Training Data ◽  
Kernel Learning ◽  
Manifold Structure ◽  
Prediction Function ◽  
Underlying Manifold ◽  
Shared Structure

We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suffer from the curse of dimensionality, at the dimension of the true underlying manifold. This observation invites an exploration of nonlocal manifold learning algorithms that attempt to discover shared structure in the tangent planes at different positions. A training criterion for such an algorithm is proposed, and experiments estimating a tangent plane prediction function are presented, showing its advantages with respect to local manifold learning algorithms: it is able to generalize very far from training data (on learning handwritten character image rotations), where local nonparametric methods fail.

Linear Mixing Model with Scaled Bundle Dictionary for Hyperspectral Unmixing with Spectral Variability

2021 ◽  
pp. 108214
Author(s):  
Saeideh Ghanbari Azar ◽  
Saeed Meshgini ◽  
Soosan Beheshti ◽  
Tohid Yousefi Rezaii
Keyword(s):  
Mixing Model ◽  
Spectral Variability ◽  
Hyperspectral Unmixing ◽  
Linear Mixing Model
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