scholarly journals Superpixel Weighted Low-rank and Sparse Approximation for Hyperspectral Unmixing

Author(s):  
Taner Ince ◽  
Tugcan Dundar ◽  
Seydi Kacmaz ◽  
Hasari Karci

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.

2022 ◽  
Author(s):  
Taner Ince ◽  
Tugcan Dundar ◽  
Seydi Kacmaz ◽  
Hasari Karci

We propose a superpixel weighted low-rank and sparse unmixing (SWLRSU) method for sparse unmixing. The proposed method consists of two steps. In the first step, we segment hyperspectral image into superpixels which are defined as the homogeneous regions with different shape and sizes according to the spatial structure. Then, an efficient method is proposed to obtain a spatial weight term using superpixels to capture the spatial structure of hyperspectral data. In the second step, we solve a superpixel guided low-rank and spatially weighted sparse approximation problem in which spatial weight term obtained in the first step is used as a weight term in sparsity promoting norm. This formulation exploits the spatial correlation of the pixels in the hyperspectral image efficiently, which yields satisfactory unmixing results. The experiments are conducted on simulated and real data sets to show the effectiveness of the proposed method.


2019 ◽  
Vol 11 (24) ◽  
pp. 2897 ◽  
Author(s):  
Yuhui Zheng ◽  
Feiyang Wu ◽  
Hiuk Jae Shim ◽  
Le Sun

Hyperspectral unmixing is a key preprocessing technique for hyperspectral image analysis. To further improve the unmixing performance, in this paper, a nonlocal low-rank prior associated with spatial smoothness and spectral collaborative sparsity are integrated together for unmixing the hyperspectral data. The proposed method is based on a fact that hyperspectral images have self-similarity in nonlocal sense and smoothness in local sense. To explore the spatial self-similarity, nonlocal cubic patches are grouped together to compose a low-rank matrix. Then, based on the linear mixed model framework, the nuclear norm is constrained to the abundance matrix of these similar patches to enforce low-rank property. In addition, the local spatial information and spectral characteristic are also taken into account by introducing TV regularization and collaborative sparse terms, respectively. Finally, the results of the experiments on two simulated data sets and two real data sets show that the proposed algorithm produces better performance than other state-of-the-art algorithms.


2020 ◽  
Vol 12 (12) ◽  
pp. 2016 ◽  
Author(s):  
Tao Zhang ◽  
Puzhao Zhang ◽  
Weilin Zhong ◽  
Zhen Yang ◽  
Fan Yang

The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fan Li

Sparse unmixing is an important technique for hyperspectral data analysis. Most sparse unmixing algorithms underutilize the spatial and spectral information of the hyperspectral image, which is unfavourable for the accuracy of endmember identification and abundance estimation. We propose a new spectral unmixing method based on the low-rank representation model and spatial-weighted collaborative sparsity, aiming to exploit structural information in both the spatial and spectral domains for unmixing. The spatial weights are incorporated into the collaborative sparse regularization term to enhance the spatial continuity of the image. Meanwhile, the global low-rank constraint is employed to maintain the spatial low-dimensional structure of the image. The model is solved by the well-known alternating direction method of multiplier, in which the abundance coefficients and the spatial weights are updated iteratively in the inner and outer loops, respectively. Experimental results obtained from simulation and real data reveal the superior performance of the proposed algorithm on spectral unmixing.


2021 ◽  
Vol 87 (6) ◽  
pp. 445-455
Author(s):  
Yi Ma ◽  
Zezhong Zheng ◽  
Yutang Ma ◽  
Mingcang Zhu ◽  
Ran Huang ◽  
...  

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.


2019 ◽  
Vol 11 (9) ◽  
pp. 1114
Author(s):  
Sixiu Hu ◽  
Jiangtao Peng ◽  
Yingxiong Fu ◽  
Luoqing Li

By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.


2020 ◽  
Vol 12 (2) ◽  
pp. 297 ◽  
Author(s):  
Nasehe Jamshidpour ◽  
Abdolreza Safari ◽  
Saeid Homayouni

This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter- and intra-view confidences. Hyperspectral data inherently owns high dimensionality, which makes it suitable for multi-view learning algorithms. Furthermore, by employing multiple learners at each view, a more accurate estimation of the underlying data distribution can be obtained. We also implemented a spectral-spatial graph-based semi-supervised learning (SSL) method as the classifier, which improved the performance of the classification task in comparison with supervised learning. The evaluation of the proposed method was based on three different benchmark hyperspectral data sets. The results were also compared with other state-of-the-art AL-SSL methods. The experimental results demonstrated the efficiency and statistically significant superiority of the proposed method. The GA-MVML AL method improved the classification performances by 16.68%, 18.37%, and 15.1% for different data sets after 40 iterations.


2021 ◽  
Vol 13 (17) ◽  
pp. 3411
Author(s):  
Lanxue Dang ◽  
Peidong Pang ◽  
Xianyu Zuo ◽  
Yang Liu ◽  
Jay Lee

Convolutional neural network (CNN) has shown excellent performance in hyperspectral image (HSI) classification. However, the structure of the CNN models is complex, requiring many training parameters and floating-point operations (FLOPs). This is often inefficient and results in longer training and testing time. In addition, the label samples of hyperspectral data are limited, and a deep network often causes the over-fitting phenomenon. Hence, a dual-path small convolution (DPSC) module is proposed. It is composed of two 1 × 1 small convolutions with a residual path and a density path. It can effectively extract abstract features from HSI. A dual-path small convolution network (DPSCN) is constructed by stacking DPSC modules. Specifically, the proposed model uses a DPSC module to complete the extraction of spectral and spectral–spatial features successively. It then uses a global average pooling layer at the end of the model to replace the conventional fully connected layer to complete the final classification. In the implemented study, all convolutional layers of the proposed network, except the middle layer, use 1 × 1 small convolution, effectively reduced model parameters and increased the speed of feature extraction processes. DPSCN was compared with several current state-of-the-art models. The results on three benchmark HSI data sets demonstrated that the proposed model is of lower complexity, has stronger generalization ability, and has higher classification efficiency.


2019 ◽  
Vol 11 (10) ◽  
pp. 1149 ◽  
Author(s):  
Fuding Xie ◽  
Cunkuan Lei ◽  
Jun Yang ◽  
Cui Jin

Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the complexity of the spatial structure of objects and the spectral similarity between different objects. In this study, an effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation (DPR) is proposed to discriminate land covers of interest. A novel technique for measuring the similarity of a pair of pixels in HSI is suggested to improve the simple linear iterative clustering (SLIC) algorithm. Unlike the existing application of SLIC technique to HSI, the improved SLIC algorithm can be directly used to segment HSI into superpixels without using principal component analysis in advance, and is free of parameters. Furthermore, the proposed three-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Compared with the existing two-step classification framework, the use of DPR technology in preprocessing significantly improves the classification accuracy. The effectiveness of the proposed method is verified on three public real hyperspectral datasets. The comparison results of several competitive methods show the superiority of this scheme.


Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3269 ◽  
Author(s):  
Hongmin Gao ◽  
Dan Yao ◽  
Mingxia Wang ◽  
Chenming Li ◽  
Haiyun Liu ◽  
...  

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.


Sign in / Sign up

Export Citation Format

Share Document