scholarly journals A Spatial-Enhanced LSE-SFIM Algorithm for Hyperspectral and Multispectral Images Fusion

2021 ◽  
Vol 13 (24) ◽  
pp. 4967
Author(s):  
Yulei Wang ◽  
Qingyu Zhu ◽  
Yao Shi ◽  
Meiping Song ◽  
Chunyan Yu
Keyword(s):  
Information Quality ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Computing Time ◽  
Spectral Characteristics ◽  
Spatial Filter ◽  
Least Square ◽  
Spatial Filters ◽  
Fused Image

The fusion of a hyperspectral image (HSI) and multispectral image (MSI) can significantly improve the ability of ground target recognition and identification. The quality of spatial information and the fidelity of spectral information are normally contradictory. However, these two properties are non-negligible indicators for multi-source remote-sensing images fusion. The smoothing filter-based intensity modulation (SFIM) method is a simple yet effective model for image fusion, which can improve the spatial texture details of the image well, and maintain the spectral characteristics of the image significantly. However, traditional SFIM has a poor effect for edge information sharpening, leading to a bad overall fusion result. In order to obtain better spatial information, a spatial filter-based improved LSE-SFIM algorithm is proposed in this paper. Firstly, the least square estimation (LSE) algorithm is combined with SFIM, which can effectively improve the spatial information quality of the fused image. At the same time, in order to better maintain the spatial information, four spatial filters (mean, median, nearest and bilinear) are used for the simulated MSI image to extract fine spatial information. Six quality indexes are used to compare the performance of different algorithms, and the experimental results demonstrate that the LSE-SFIM based on bilinear (LES-SFIM-B) performs significantly better than the traditional SFIM algorithm and other spatially enhanced LSE-SFIM algorithms proposed in this paper. Furthermore, LSE-SFIM-B could also obtain similar performance compared with three state-of-the-art HSI-MSI fusion algorithms (CNMF, HySure, and FUSE), while the computing time is much shorter.

“I Can Live Without Banks, but Not Without Banking”

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Nitika Sharma ◽  
Pooja Goel ◽  
Anuj Sharma
Keyword(s):  
Structural Equation ◽  
Information Quality ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Online Questionnaire ◽  
Website Quality ◽  
Mediating Role ◽  
Service Interaction

The purpose of this paper is to examine the antecedents of e-banking loyalty and evangelism via threefold construct of WEQUAL (usability, information quality, and service interaction) of public sector banks operating in India. Moreover, it also investigates the mediating role of consumers' trust on the website quality of these banks and their impact on e-banking loyalty and evangelism. The data was collected from 243 respondents through online questionnaire. In order to develop the model and test the hypotheses, partial least square structural equation modeling (PLS-SEM) was done through Smart PLS version 3.2.9. Results assert that website quality of banks positively influences the trust of consumers via usability, information quality, and service interaction. Also, consumer trust plays a mediation role between WEBQUAL constructs and e-banking loyalty and evangelism.

INFORMATION QUALITY AND THE ORGANIZATIONAL EFFECTIVENESS: THE MODERATING EFFECTS OF ORGANIZATIONAL CULTURE AMONG CONVENTIONAL AND ISLAMIC BANKS

2019 ◽  
Vol 5 (10) ◽  
pp. 01-10
Author(s):  
Basel J. A. Ali
Keyword(s):  
Organizational Culture ◽  
Organizational Performance ◽  
Business Performance ◽  
Information Quality ◽  
Partial Least Square ◽  
Least Square ◽  
Islamic Banks ◽  
Quality Of Information ◽  
Conventional And Islamic Banks

Quality of information is a priceless asset for organization to possess as its assist in carrying out business plans and changes. These business changes usually support the management executive in decision makings. In view of that, this study examines the information quality in AIS and its effects on organizational performance among conventional and Islamic banks in Jordan. To achieve that, proportionate stratified random sampling is applied to the information system users of sixteen conventional and Islamic banks in Jordan. Total copies of 600 questionnaires were distributed and only 250 among the returned copies were valid, suggesting a valid response rate of 41.7%. The study adopts the partial least square (Smart PLS 3) method to enhance the data analysis and perform hypotheses testing. Findings clearly show that quality of information is the key for business growth as it indicates a positive effect on organizational performance. Further result shows that organizational culture improves and increases business performance when combined with information quality. For this reason, conventional and Islamic banks in Jordan should have well-developed AIS as it assists organizations to -attain higher performance. There is need for more development in management skills to fully exploit the AIS in order to realize a greater organizational performance. In other words, full implementation of AIS should be given more priority by the managements of these conventional and Islamic banks.

scholarly journals Empirical Investigation of participation on crowdsourcing platforms- A gamified approach

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Extrinsic Motivation ◽  
Information Quality ◽  
Self Determination Theory ◽  
Partial Least Square ◽  
Least Square ◽  
Equation Modeling ◽  
Quality Of Information ◽  
Structured Equation Modeling ◽  
Game Elements

Crowdsourcing platforms have gained importance in recent times, and their success is dependent mainly on the participation of the crowd. Participation is a function of both intrinsic and extrinsic motivation. Moreover, with the growing scale of information, the participants would need to focus on the quality of information to achieve sustainable participation. Our study uses game elements and information quality grounded in Motivational Affordance Perspective (MAP) to study the intrinsic and extrinsic participation on a crowdsourcing platform. We collected responses from 337 participants who are actively contributing to any crowdsourcing platform. Warp PLS uses partial least square structured equation modeling. The results confirm that the use of game elements positively promotes the participant’s intrinsic and extrinsic participation. We also confirmed that motivation is also positively moderated by the quality of information that the crowdsourcing platform shares with the participants. The results help in extending the theoretical arguments of MAP and self-determination theory.

Improved Low-Rank Matrix Approximation for Hyperspectral Image Spatial-Spectral Feature Extraction

2014 ◽  
Vol 590 ◽  
pp. 716-721
Author(s):  
Qian Zhang ◽  
Yi Ping Yang ◽  
Xin Wei Jiang
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Least Square ◽  
Low Rank ◽  
Matrix Approximation ◽  
Data Set ◽  
Rank Matrix ◽  
Low Rank Matrix ◽  
Low Rank Matrix Approximation

It is important to take account into both the spectral domain and spatial domain information for hyperspectral image analysis. Thus, how to effectively integrate both spectral and spatial information confronts us. Motived by the least square form of PCA, we extend it to a low-rank matrix approximation form for multi-feature dimensionality redu-ction. In addition, we use the ensemble manifold regularize-ation techniques to capture the complementary information provided by spectral-spatial features of hyperspectral image. Experimental results on public hyperspectral data set demonstrate the effectiveness of our proposed method.

scholarly journals Hyperspectral Pansharpening Based on Spectral Constrained Adversarial Autoencoder

2019 ◽  
Vol 11 (22) ◽  
pp. 2691 ◽  
Author(s):  
Gang He ◽  
Jiaping Zhong ◽  
Jie Lei ◽  
Yunsong Li ◽  
Weiying Xie
Keyword(s):  
Spatial Information ◽  
Spectral Characteristics ◽  
Data Sets ◽  
Information Preservation ◽  
Fusion Methods ◽  
Adaptive Fusion ◽  
Spatial Domains ◽  
Fusion Approach ◽  
Better Than

Hyperspectral (HS) imaging is conducive to better describing and understanding the subtle differences in spectral characteristics of different materials due to sufficient spectral information compared with traditional imaging systems. However, it is still challenging to obtain high resolution (HR) HS images in both the spectral and spatial domains. Different from previous methods, we first propose spectral constrained adversarial autoencoder (SCAAE) to extract deep features of HS images and combine with the panchromatic (PAN) image to competently represent the spatial information of HR HS images, which is more comprehensive and representative. In particular, based on the adversarial autoencoder (AAE) network, the SCAAE network is built with the added spectral constraint in the loss function so that spectral consistency and a higher quality of spatial information enhancement can be ensured. Then, an adaptive fusion approach with a simple feature selection rule is induced to make full use of the spatial information contained in both the HS image and PAN image. Specifically, the spatial information from two different sensors is introduced into a convex optimization equation to obtain the fusion proportion of the two parts and estimate the generated HR HS image. By analyzing the results from the experiments executed on the tested data sets through different methods, it can be found that, in CC, SAM, and RMSE, the performance of the proposed algorithm is improved by about 1.42%, 13.12%, and 29.26% respectively on average which is preferable to the well-performed method HySure. Compared to the MRA-based method, the improvement of the proposed method in in the above three indexes is 17.63%, 0.83%, and 11.02%, respectively. Moreover, the results are 0.87%, 22.11%, and 20.66%, respectively, better than the PCA-based method, which fully illustrated the superiority of the proposed method in spatial information preservation. All the experimental results demonstrate that the proposed method is superior to the state-of-the-art fusion methods in terms of subjective and objective evaluations.

An efficient superpixel-based sparse representation framework for hyperspectral image classification

2017 ◽  
Vol 15 (06) ◽  
pp. 1750061 ◽  
Author(s):  
Sen Jia ◽  
Bin Deng ◽  
Qiang Huang
Keyword(s):  
Sparse Representation ◽  
Visual Recognition ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Small Region ◽  
Data Sets ◽  
Training Samples ◽  
The Rich ◽  
Highly Correlated

As a powerful classifier, sparse representation-based classification (SRC) has successfully been applied in various visual recognition problems. However, due to the highly correlated bands and insufficient training samples of hyperspectral image (HSI) data, it still remains a challenging problem to effectively apply SRC in HSI. Considering the rich information of spatial structure of materials in HSI, that means the adjacent pixels belong to the same class with a high probability, in this paper, we propose an efficient superpixel-based sparse representation framework for HSI classification. Each superpixel can be regarded as a small region consisting of a number of pixels with similar spectral characteristics. The proposed framework utilizes superpixel to exploit spatial information which can greatly improve classification accuracy. Specifically, SRC is firstly used to classify the HSI data. Meanwhile, an efficient segmentation algorithm is applied to divide the HSI into many disjoint superpixels. Then, each superpixel is used to fuse the SRC classification results in superpixel level. Experimental results on two real-world HSI data sets have shown that the proposed superpixel-based SRC (SP-SRC) framework has a significant improvement over the pixel-based SRC method.

Hyperspectral image unmixing involving spatial information by extending the alternating least-squares algorithm

2015 ◽  
Vol 82 (4) ◽  
Author(s):  
Sebastian Bauer ◽  
Johannes Stefan ◽  
Fernando Puente León
Keyword(s):  
Least Squares ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Computing Time ◽  
Laboratory Data ◽  
Ground Truth ◽  
Alternating Least Squares ◽  
Spatial Constraints ◽  
Alternating Least Squares Algorithm ◽  
Least Squares Algorithm

AbstractHyperspectral images, in contrast to common RGB images, offer the possibility to not only determine the pure materials present in a scene, but also material abundances in mixtures. The calculation of the material fractions with the so-called linear mixing model is not unique, an infinite number of solutions exists. Therefore, additional constraints should be incorporated. Some algorithms involve spatial constraints explicitly, e. g., they assume that the abundances mostly do not change considerably from one pixel to another. Recently, we presented such algorithms. The calculation time with spatial constraints included, however, is rather long, so it was checked if there is a faster way to include the spatial information. In this paper, we extend the well-known alternating least-squares algorithm to implicitly include the previously used spatial information in a slightly different way, namely by adding an extra image denoising step to the calculation. The extended algorithm is called ALSmooth. We compare the computing time and the results of the ALSmooth and the previously presented algorithms. For this purpose, laboratory data of mixtures with known ground truth had been acquired. Both the previously investigated algorithms and the ALSmooth algorithm are quite sensitive towards parameter value changes; the ALSmooth algorithm is even more sensitive. For certain applications with defined environment and endmembers, however, it can be a faster alternative.

scholarly journals Convolution Neural Network Based on Two-Dimensional Spectrum for Hyperspectral Image Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hongmin Gao ◽  
Shuo Lin ◽  
Yao Yang ◽  
Chenming Li ◽  
Mingxiang Yang
Keyword(s):  
Neural Network ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spectral Characteristics ◽  
Convolution Neural Network ◽  
Image Method ◽  
Two Dimensional ◽  
Input Unit ◽  
Dimensional Spectrum ◽  
Kennedy Space

Inherent spectral characteristics of hyperspectral image (HSI) data are determined and need to be deeply mined. A convolution neural network (CNN) model of two-dimensional spectrum (2D spectrum) is proposed based on the advantages of deep learning to extract feature and classify HSI. First of all, the traditional data processing methods which use small area pixel block or one-dimensional spectral vector as input unit bring many heterogeneous noises. The 2D-spectrum image method is proposed to solve the problem and make full use of spectral value and spatial information. Furthermore, a batch normalization algorithm (BN) is introduced to address internal covariate shifts caused by changes in the distribution of input data and expedite the training of the network. Finally, Softmax loss models are proposed to induce competition among the outputs and improve the performance of the CNN model. The HSI datasets of experiments include Indian Pines, Salinas, Kennedy Space Center (KSC), and Botswana. Experimental results show that the overall accuracies of the 2D-spectrum CNN model can reach 98.26%, 97.28%, 96.22%, and 93.64%. These results are higher than the accuracies of other traditional methods described in this paper. The proposed model can achieve high target classification accuracy and efficiency.

scholarly journals Pengembangan Model Keberlanjutan e-filing di Palembang Menggunakan Partial Least Square Structural Equation Models (PLS-SEM)

2019 ◽  
Vol 9 (1) ◽  
pp. 94
Author(s):  
Imelda Saluza ◽  
Dewi Sartika
Keyword(s):  
Service Quality ◽  
Conceptual Model ◽  
Structural Equation ◽  
Information Quality ◽  
Structural Equation Models ◽  
Partial Least Square ◽  
Least Square ◽  
System Quality ◽  
Tax Returns

DGT (Directorate General of Taxation) continues to optimize the collection of annual tax returns by facilitating technology-based tax service systems, one of which is e-filing that has been running since 2016. However, e-filing turned out to have less influence on the delivery of annual tax returns as reflected in the electronic annual tax returns monitoring data that only met 78% of the 2017 target. This is caused by various problems that arise during the use of e-filing such as individual technology capabilities, loss of efin, forgetting DGT Online account passwords to lack of awareness about the importance of submitting annual tax returns. Problems encounter during the use of e-filing are the basis for evaluating the continued use of e-filing in Palembang. The development of a conceptual model was conducted to evaluate the sustainability of the use of e-filing. The development of a conceptual model basically has a scarcity of supporting theories used and has a complex model. To overcome this problem, Partial Least Squares (PLS) Structural Equation Model (SEM) could be applied to. The results of data analysis found that information quality and service quality did not have a positive influence on the sustainability of the use of e-filing and the level of correlation between information quality, system quality, service quality, and individual ability was still small towards the sustainability of the use of e-filing. It could be concluded that the quality of information and service provided by DGT has the opposite effect that does not even influance the sustainability of e-filing usage. However, the quality of the system and the ability of individuals to give effect to e-filing usage in reporting in reporting annual tax returns. Therefore, DGT is expected to improve the quality of the system of e-filing expecially in the function of e-filing, ease of use, usefullness of e-filing in reporting annual tax return. In additory it is necessary to carry out socialization and training on e-filing to improve the ability of individuals to report online their annual tax report. The findings of this research are very important for the Tax office in Palembang to analyze the sustainability of the use of e-filing that has been proven empirically, multidimensional and in a specific context. The result of the study could be used as a reference to improve overall quality of taxation for the sake of sustainable use of e-filing. 

Survey on a Fast Hyperspectral Image Classification Method Combined with Spatial Information

2013 ◽  
Vol 299 ◽  
pp. 168-171
Author(s):  
Qian Wang
Keyword(s):  
Spatial Information ◽  
Hyperspectral Image ◽  
Linear Method ◽  
Computing Time ◽  
Classification Method ◽  
Hyperspectral Image Classification ◽  
Processing Data ◽  
Simulation Results ◽  
Fast Classification ◽  
Region Feature

A fast classification method of hyperspectral image is presented to resolve these problems caused by large processing data and noise influence. First, space information is used to extract Spatial Region Feature Spectral. Next, the non-linear method of feature extraction is used to extract the feature of SRFS. The simulation results show that the method can significantly improve the classification results of classifiers and reduce computing time.

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