scholarly journals Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing

2022 ◽  
Vol 14 (2) ◽  
pp. 383
Author(s):  
Xinxi Feng ◽  
Le Han ◽  
Le Dong
Keyword(s):  
Hyperspectral Image ◽  
Spatial Dimension ◽  
Tensor Factorization ◽  
Group Sparsity ◽  
Sparse Regularization ◽  
Hyperspectral Unmixing ◽  
Spectral Bands ◽  
Alternating Direction ◽  
Tv Regularization ◽  
Mixed Pixels

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the L2,1 norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method.

Hyperspectral image compression using hybrid transform with different wavelet-based transform coding

2019 ◽  
Vol 18 (01) ◽  
pp. 1941008 ◽  
Author(s):  
R. Nagendran ◽  
A. Vasuki
Keyword(s):  
Spatial Data ◽  
Hyperspectral Image ◽  
Spatial Dimension ◽  
Image Resolution ◽  
Hyperspectral Images ◽  
Experimental Result ◽  
Spectral Bands ◽  
Karhunen Loeve Transform ◽  
Hyperspectral Image Compression ◽  
Integer Wavelet

Hyperspectral image resolution offers limited spectral bands within a continual spectral spectrum, creating one of the spectra of most pixels inside the sequence which contains huge volume of data. Data transmission and storage is a challenging task. Compression of hyperspectral images are inevitable. This work proposes a Hyperspectral Image (HSI) compression using Hybrid Transform. First the HSI is decomposed into 1D and it is clustered and tiled. Each cluster is applied with Integer Karhunen–Loeve Transform (IKLT) and as such it is applied for whole image to get IKLT bands in spectral dimension. Then IKLT bands are applied with Integer Wavelet Transform (IDWT) to decorrelate the spatial data in spatial dimension. The combination of IKLT and IDWT is known as Hybrid transform. Second, the decorrelated wavelet coefficients are applied to Spatial-oriention Tree Wavelet (STW), Wavelet Difference Reduction (WDR) and Adaptively Scanned Wavelet Difference Reduction (ASWDR). The experimental result shows STW algorithm using Hybrid Transform gives better PSNR (db) and bits per pixel per band (bpppb) for hyperspectral images. The comparison between STW, WDR and ASWDR with Hybrid Transform for Indian Pines, Salinas, Botswana, Botswana and KSC images is experimented.

Endmember independence constrained hyperspectral unmixing via nonnegative tensor factorization

2021 ◽  
Vol 216 ◽  
pp. 106657
Author(s):  
Jin-Ju Wang ◽  
Ding-Cheng Wang ◽  
Ting-Zhu Huang ◽  
Jie Huang ◽  
Xi-Le Zhao ◽  
...  
Keyword(s):  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
Hyperspectral Unmixing

Novel method for hyperspectral unmixing: fuzzy c-means unmixing

Sensor Review ◽  
2016 ◽  
Vol 36 (2) ◽  
pp. 184-192
Author(s):  
Mingyu Nie ◽  
Zhi Liu ◽  
Xiaomei Li ◽  
Qiang Wu ◽  
Bo Tang ◽  
...  
Keyword(s):  
Design Methodology ◽  
Hyperspectral Image ◽  
A Priori ◽  
Image Sensors ◽  
Spectral Unmixing ◽  
Content Type ◽  
Hyperspectral Unmixing ◽  
Fuzzy C Means ◽  
Novel Method ◽  
Iteration Analysis

Purpose This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori. Design/methodology/approach This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time. Findings Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy. Originality/value The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

scholarly journals SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

2020 ◽  
Vol 12 (18) ◽  
pp. 2979
Author(s):  
Le Sun ◽  
Chengxun He ◽  
Yuhui Zheng ◽  
Songze Tang
Keyword(s):  
Hyperspectral Image ◽  
Dimensional Subspace ◽  
Low Rank ◽  
Spatial Correlations ◽  
Self Similarity ◽  
Multiple Datasets ◽  
Alternating Direction ◽  
Non Local ◽  
Low Dimensional ◽  
Spatial Redundancy

During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.

Combined Nonlocal Spatial Information and Spatial Group Sparsity in NMF for Hyperspectral Unmixing

2020 ◽  
Vol 17 (10) ◽  
pp. 1767-1771
Author(s):  
Longshan Yang ◽  
Junhuan Peng ◽  
Huiwei Su ◽  
Linlin Xu ◽  
Yuebin Wang ◽  
...  
Keyword(s):  
Spatial Information ◽  
Group Sparsity ◽  
Hyperspectral Unmixing

scholarly journals Combining Spectral Unmixing and 3D/2D Dense Networks with Early-Exiting Strategy for Hyperspectral Image Classification

2020 ◽  
Vol 12 (5) ◽  
pp. 779 ◽  
Author(s):  
Bei Fang ◽  
Yunpeng Bai ◽  
Ying Li
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Hyperspectral Image ◽  
Spectral Unmixing ◽  
Classification Methods ◽  
Dense Network ◽  
Training Samples ◽  
Mixed Pixels ◽  
Benchmark Datasets ◽  
Hard Samples

Recently, Hyperspectral Image (HSI) classification methods based on deep learning models have shown encouraging performance. However, the limited numbers of training samples, as well as the mixed pixels due to low spatial resolution, have become major obstacles for HSI classification. To tackle these problems, we propose a resource-efficient HSI classification framework which introduces adaptive spectral unmixing into a 3D/2D dense network with early-exiting strategy. More specifically, on one hand, our framework uses a cascade of intermediate classifiers throughout the 3D/2D dense network that is trained end-to-end. The proposed 3D/2D dense network that integrates 3D convolutions with 2D convolutions is more capable of handling spectral-spatial features, while containing fewer parameters compared with the conventional 3D convolutions, and further boosts the network performance with limited training samples. On another hand, considering the existence of mixed pixels in HSI data, the pixels in HSI classification are divided into hard samples and easy samples. With the early-exiting strategy in these intermediate classifiers, the average accuracy can be improved by reducing the amount of computation cost for easy samples, thus focusing on classifying hard samples. Furthermore, for hard samples, an adaptive spectral unmixing method is proposed as a complementary source of information for classification, which brings considerable benefits to the final performance. Experimental results on four HSI benchmark datasets demonstrate that the proposed method can achieve better performance than state-of-the-art deep learning-based methods and other traditional HSI classification methods.

scholarly journals Building a Compact Convolutional Neural Network for Embedded Intelligent Sensor Systems Using Group Sparsity and Knowledge Distillation

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4307 ◽  
Author(s):  
Jungchan Cho ◽  
Minsik Lee
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Group Sparsity ◽  
Control Logic ◽  
Sparse Regularization ◽  
Trade Off ◽  
Main Research ◽  
Deep Network ◽  
Intelligent Sensors ◽  
Knowledge Distillation

As artificial intelligence (AI)- or deep-learning-based technologies become more popular,the main research interest in the field is not only on their accuracy, but also their efficiency, e.g., theability to give immediate results on the users’ inputs. To achieve this, there have been many attemptsto embed deep learning technology on intelligent sensors. However, there are still many obstacles inembedding a deep network in sensors with limited resources. Most importantly, there is an apparenttrade-off between the complexity of a network and its processing time, and finding a structure witha better trade-off curve is vital for successful applications in intelligent sensors. In this paper, wepropose two strategies for designing a compact deep network that maintains the required level ofperformance even after minimizing the computations. The first strategy is to automatically determinethe number of parameters of a network by utilizing group sparsity and knowledge distillation (KD)in the training process. By doing so, KD can compensate for the possible losses in accuracy causedby enforcing sparsity. Nevertheless, a problem in applying the first strategy is the unclarity indetermining the balance between the accuracy improvement due to KD and the parameter reductionby sparse regularization. To handle this balancing problem, we propose a second strategy: a feedbackcontrol mechanism based on the proportional control theory. The feedback control logic determinesthe amount of emphasis to be put on network sparsity during training and is controlled based onthe comparative accuracy losses of the teacher and student models in the training. A surprising facthere is that this control scheme not only determines an appropriate trade-off point, but also improvesthe trade-off curve itself. The results of experiments on CIFAR-10, CIFAR-100, and ImageNet32 X 32datasets show that the proposed method is effective in building a compact network while preventingperformance degradation due to sparsity regularization much better than other baselines.

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