scholarly journals Hyperspectral Image Denoising Based on Spectral Dictionary Learning and Sparse Coding

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 86 ◽  
Author(s):  
Xiaorui Song ◽  
Lingda Wu ◽  
Hongxing Hao ◽  
Wanpeng Xu
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Coding Theory ◽  
Hyperspectral Image ◽  
Contextual Information ◽  
A Priori ◽  
Noise Model ◽  
Noise Component ◽  
Comparison Algorithms ◽  
Better Than

Processing and applications of hyperspectral images (HSI) are limited by the noise component. This paper establishes an HSI denoising algorithm by applying dictionary learning and sparse coding theory, which is extended into the spectral domain. First, the HSI noise model under additive noise assumption was studied. Considering the spectral information of HSI data, a novel dictionary learning method based on an online method is proposed to train the spectral dictionary for denoising. With the spatial–contextual information in the noisy HSI exploited as a priori knowledge, the total variation regularizer is introduced to perform the sparse coding. Finally, sparse reconstruction is implemented to produce the denoised HSI. The performance of the proposed approach is better than the existing algorithms. The experiments illustrate that the denoising result obtained by the proposed algorithm is at least 1 dB better than that of the comparison algorithms. The intrinsic details of both spatial and spectral structures can be preserved after significant denoising.

scholarly journals Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

2019 ◽  
Vol 5 (11) ◽  
pp. 85 ◽  
Author(s):  
Ayan Chatterjee ◽  
Peter W. T. Yuen
Keyword(s):  
Dictionary Learning ◽  
Sparse Coding ◽  
Clustering Algorithm ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Scene Reconstruction ◽  
Data Sets ◽  
Practical Applications ◽  
Input Dataset ◽  
Better Than

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

scholarly journals Neural Network for Aerosol Retrieval from Hyperspectral Imagery

2019 ◽  
Author(s):  
Steffen Mauceri ◽  
Bruce Kindel ◽  
Steven Massie ◽  
Peter Pilewskie
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Surface Albedo ◽  
Hyperspectral Image ◽  
A Priori ◽  
Image Data ◽  
Aerosol Retrieval ◽  
Hyperspectral Image Data ◽  
Priori Information ◽  
Better Than

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon-, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol- concentration and type, surface albedo, water vapor and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information of the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.

scholarly journals Neural network for aerosol retrieval from hyperspectral imagery

2019 ◽  
Vol 12 (11) ◽  
pp. 6017-6036 ◽  
Author(s):  
Steffen Mauceri ◽  
Bruce Kindel ◽  
Steven Massie ◽  
Peter Pilewskie
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Surface Albedo ◽  
Hyperspectral Image ◽  
A Priori ◽  
Image Data ◽  
Aerosol Retrieval ◽  
Hyperspectral Image Data ◽  
Priori Information ◽  
Better Than

Abstract. We retrieve aerosol optical thickness (AOT) independently for brown carbon, dust and sulfate from hyperspectral image data. The model, a neural network, is trained on atmospheric radiative transfer calculations from MODTRAN 6.0 with varying aerosol concentration and type, surface albedo, water vapor, and viewing geometries. From a set of test radiative transfer calculations, we are able to retrieve AOT with a standard error of better than ±0.05. No a priori information on the surface albedo or atmospheric state is necessary for our model. We apply the model to AVIRIS-NG imagery from a recent campaign over India and demonstrate its performance under high and low aerosol loadings and different aerosol types.

From Isovist to Spatial Perception: Wayfinding in Historic Quarter

2016 ◽  
Vol 1 (3) ◽  
Author(s):  
Chih-Hung Chen ◽  
Ting-Ju Lin ◽  
Chih-Yu Chen
Keyword(s):  
Social Interactions ◽  
Spatial Perception ◽  
Publishing House ◽  
A Priori ◽  
Relative Area ◽  
Social Stimuli ◽  
Historical District ◽  
International Publishing ◽  
A Priori Analysis ◽  
Better Than

Based on the assumption that human behaviours are mainly affected by physical and animate environments, this empirical research takes the changeful and complex historical district in Tainan to observe wayfinding behaviours. An a priori analysis of the isovist fields is conducted to identify spatial characteristics. Three measures, the relative area, convexity, and circularity, are applied to scrutinize the possible stopping points, change of speed, and route choices. Accordingly, an experiment is carried out to observe spatial behaviours and different influences of social stimuli. Results show that social interactions afford groups and pairs to perform better than individual observers in wayfinding.© 2016. The Authors. Published for AMER ABRA by e-International Publishing House, Ltd., UK. Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies, Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, MalaysiaKeywords: wayfinding; isovist; spatial perception and social stimuli; historic quarter

scholarly journals Sensorimotor priors in nonstationary environments

2013 ◽  
Vol 109 (5) ◽  
pp. 1259-1267 ◽  
Author(s):  
Devika Narain ◽  
Robert J. van Beers ◽  
Jeroen B. J. Smeets ◽  
Eli Brenner
Keyword(s):  
Environmental Variables ◽  
A Priori ◽  
Past Research ◽  
Systematic Change ◽  
Recent Experience ◽  
Environmental Variance ◽  
Adaptive Models ◽  
Priori Information ◽  
Human Nervous System ◽  
Better Than

In the course of its interaction with the world, the human nervous system must constantly estimate various variables in the surrounding environment. Past research indicates that environmental variables may be represented as probabilistic distributions of a priori information (priors). Priors for environmental variables that do not change much over time have been widely studied. Little is known, however, about how priors develop in environments with nonstationary statistics. We examine whether humans change their reliance on the prior based on recent changes in environmental variance. Through experimentation, we obtain an online estimate of the human sensorimotor prior (prediction) and then compare it to similar online predictions made by various nonadaptive and adaptive models. Simulations show that models that rapidly adapt to nonstationary components in the environments predict the stimuli better than models that do not take the changing statistics of the environment into consideration. We found that adaptive models best predict participants' responses in most cases. However, we find no support for the idea that this is a consequence of increased reliance on recent experience just after the occurrence of a systematic change in the environment.

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.

Sign in / Sign up

Export Citation Format

Share Document