scholarly journals ADVANCES IN HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION AND SPECTRAL UNMIXING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 451-458 ◽  
Author(s):  
C. Lanaras ◽  
E. Baltsavias ◽  
K. Schindler
Keyword(s):  
Image Fusion ◽  
Hyperspectral Image ◽  
Mathematical Optimization ◽  
Spectral Unmixing ◽  
Semantic Interpretation ◽  
Multispectral Images ◽  
Hyperspectral Unmixing ◽  
Environmental Mapping ◽  
Fused Image ◽  
Linear Spectral Unmixing

In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral endmembers at subpixel level w.r.t. the pixel size of the hyperspectral image. We assume that the two images are radiometrically corrected and geometrically co-registered. The scientific contributions of this work are (a) a simultaneous approach to image fusion and hyperspectral unmixing, (b) enforcing several physically plausible constraints during unmixing that are all well-known, but typically not used in combination, and (c) the use of efficient, state-of-the-art mathematical optimization tools to implement the processing. The results of our joint fusion and unmixing has the potential to enable more accurate and detailed semantic interpretation of objects and their properties in hyperspectral and multispectral images, with applications in environmental mapping, monitoring and change detection. In our experiments, the proposed method always improves the fusion compared to competing methods, reducing RMSE between 4% and 53%.

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.

Image Fusion in Hyperspectral Image Classification using Genetic Algorithm

2016 ◽  
Vol 2 (3) ◽  
pp. 703 ◽  
Author(s):  
B. Saichandana ◽  
K. Srinivas ◽  
R. KiranKumar
Keyword(s):  
Genetic Algorithm ◽  
Image Fusion ◽  
Image Classification ◽  
Empirical Mode Decomposition ◽  
Hyperspectral Image ◽  
Image Data ◽  
Hyperspectral Data ◽  
Hyperspectral Image Classification ◽  
Mode Decomposition ◽  
Fused Image

<p>Hyperspectral remote sensors collect image data for a large number of narrow, adjacent spectral bands. Every pixel in hyperspectral image involves a continuous spectrum that is used to classify the objects with great detail and precision. This paper presents hyperspectral image classification mechanism using genetic algorithm with empirical mode decomposition and image fusion used in preprocessing stage. 2-D Empirical mode decomposition method is used to remove any noisy components in each band of the hyperspectral data. After filtering, image fusion is performed on the hyperspectral bands to selectively merge the maximum possible features from the source images to form a single image. This fused image is classified using genetic algorithm. Different indices, such as K-means (KMI), Davies-Bouldin Index (DBI), and Xie-Beni Index (XBI) are used as objective functions. This method increases classification accuracy of hyperspectral image.</p>

scholarly journals Superpixel Spectral Unmixing for Hyperspectral Image Superresolution Using a Coupled Encoder-Decoder Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shao-lei Zhang ◽  
Guang-yuan Fu ◽  
Hong-qiao Wang ◽  
Yu-qing Zhao
Keyword(s):  
Hyperspectral Image ◽  
Visual Quality ◽  
Spectral Unmixing ◽  
The Other ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Spectral Information ◽  
Multispectral Images ◽  
Self Similarity ◽  
Rank Constraint

In this paper, we propose a novel hyperspectral image superresolution method based on superpixel spectral unmixing using a coupled encoder-decoder network. The hyperspectral image and multispectral images are fused to generate high-resolution hyperspectral images through the spectral unmixing framework with low-rank constraint. Specifically, the endmember and abundance information is extracted via a coupled encoder-decoder network integrating the priori for unmixing. The coupled network consists of two encoders and one shared decoder, where spectral information is preserved through the encoder. The multispectral image is clustered into superpixels to explore self-similarity, and then, the superpixels are unmixed to obtain an abundance matrix. By imposing a low-rank constraint on the abundance matrix, we further improve the superresolution performance. Experiments on the CAVE and Harvard datasets indicate that our superresolution method outperforms the other compared methods in terms of quantitative evaluation and visual quality.

scholarly journals Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation

2019 ◽  
Vol 8 (3) ◽  
pp. 1081-1087
Author(s):  
K. Mallikharjuna Rao ◽  
B. Srinivasa Rao ◽  
B. Sai Chandana ◽  
J. Harikiran
Keyword(s):  
Dimensionality Reduction ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Multispectral Images ◽  
Pixel Intensity ◽  
Fused Image ◽  
Histogram Similarity ◽  
Spectral Channels

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channels with dimensionality reduction method using subset selection and hierarchical clustering. A methodology based on subset construction is used for selecting k informative bands from d bands dataset. In this selection, similarity metrics such as Average Pixel Intensity [API], Histogram Similarity [HS], Mutual Information [MI] and Correlation Similarity [CS] are used to create k distinct subsets and from each subset, a single band is selected. The informative bands which are selected are merged into a single image using hierarchical fusion technique. After getting fused image, Hierarchical clustering algorithm is used for segmentation of image. The qualitative and quantitative analysis shows that CS similarity metric in dimensionality reduction algorithm gets high quality segmented image.

scholarly journals Fusion of Hyperspectral and Multispectral Images Based on a Centralized Non-local Sparsity Model of Abundance Maps

Tecnura ◽  
2020 ◽  
Vol 24 (66) ◽  
pp. 62-75
Author(s):  
Edwin Vargas ◽  
Kevin Arias ◽  
Fernando Rojas ◽  
Henry Arguello
Keyword(s):  
Image Fusion ◽  
Sparse Representation ◽  
Spatial Resolution ◽  
Real Data ◽  
Spectral Unmixing ◽  
Multispectral Images ◽  
Self Similarity ◽  
Diverse Range ◽  
Learned Dictionary ◽  
Non Local

Objective: Hyperspectral (HS) imaging systems are commonly used in a diverse range of applications that involve detection and classification tasks. However, the low spatial resolution of hyperspectral images may limit the performance of the involved tasks in such applications. In the last years, fusing the information of an HS image with high spatial resolution multispectral (MS) or panchromatic (PAN) images has been widely studied to enhance the spatial resolution. Image fusion has been formulated as an inverse problem whose solution is an HS image which assumed to be sparse in an analytic or learned dictionary. This work proposes a non-local centralized sparse representation model on a set of learned dictionaries in order to regularize the conventional fusion problem.Methodology: The dictionaries are learned from the estimated abundance data taking advantage of the depth correlation between abundance maps and the non-local self- similarity over the spatial domain. Then, conditionally on these dictionaries, the fusion problem is solved by an alternating iterative numerical algorithm.Results: Experimental results with real data show that the proposed method outperforms the state-of-the-art methods under different quantitative assessments.Conclusions: In this work, we propose a hyperspectral and multispectral image fusion method based on a non-local centralized sparse representation on abundance maps. This model allows us to include the non-local redundancy of abundance maps in the fusion problem using spectral unmixing and improve the performance of the sparsity-based fusion approaches.

scholarly journals Spectral Unmixing With Perturbed Endmembers

2017 ◽  
Author(s):  
Reza Arablouei
Keyword(s):  
Fisher Information Matrix ◽  
Hyperspectral Image ◽  
Information Matrix ◽  
Spectral Unmixing ◽  
Spectral Image ◽  
Alternating Direction ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Noise Error ◽  
The Given

We consider the problem of supervised spectral unmixing with a fully-perturbed linear mixture model where the given endmembers, as well as the observations of the spectral image, are subject to perturbation due to noise, error, mismatch, etc. We calculate the Fisher information matrix and the Cramer-Rao lower bound associated with the estimation of the abundance matrix in the considered fully-perturbed linear spectral unmixing problem. We develop an algorithm for estimating the abundance matrix by minimizing a constrained and regularized maximum-loglikelihood objective function using the block coordinate-descend iterations and the alternating direction method of multipliers. We analyze the convergence of the proposed algorithm theoretically and perform simulations with real hyperspectral image datasets to evaluate its performance. The simulation results corroborate the efficacy of the proposed algorithm in mitigating the adverse effects of perturbation in the endmembers.

scholarly journals How Hyperspectral Image Unmixing and Denoising Can Boost Each Other

2020 ◽  
Vol 12 (11) ◽  
pp. 1728
Author(s):  
Behnood Rasti ◽  
Bikram Koirala ◽  
Paul Scheunders ◽  
Pedram Ghamisi
Keyword(s):  
Noise Reduction ◽  
Hyperspectral Image ◽  
Optimization Criterion ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Low Rank ◽  
Hyperspectral Unmixing ◽  
Selection Strategies ◽  
Linear Unmixing ◽  
The One

Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the one used in HSI unmixing. However, the optimization criterion and the assumptions on the constraints are different. Additionally, noise reduction as a preprocessing step in hyperspectral data analysis is often ignored. The main goal of this paper is to study experimentally the influence of noise on the process of hyperspectral unmixing by: (1) investigating the effect of noise reduction as a preprocessing step on the performance of hyperspectral unmixing; (2) studying the relation between noise and different endmember selection strategies; (3) investigating the performance of HSI unmixing as an HSI denoiser; (4) comparing the denoising performance of spectral unmixing, state-of-the-art HSI denoising techniques, and the combination of both. All experiments are performed on simulated and real datasets.

scholarly journals Small Multispectral UAV Sensor and Its Image Fusion Capability in Cultural Heritage Applications

Heritage ◽  
2020 ◽  
Vol 3 (4) ◽  
pp. 1046-1062
Author(s):  
Dimitris Kaimaris ◽  
Aristoteles Kandylas
Keyword(s):  
Image Fusion ◽  
Spatial Resolution ◽  
Archaeological Site ◽  
Digital Processing ◽  
Archaeological Sites ◽  
Technological Evolution ◽  
Multispectral Images ◽  
Fused Image ◽  
Spectral Fidelity ◽  
Digital Measurements

For many decades the multispectral images of the earth’s surface and its objects were taken from multispectral sensors placed on satellites. In recent years, the technological evolution produced similar sensors (much smaller in size and weight) which can be placed on Unmanned Aerial Vehicles (UAVs), thereby allowing the collection of higher spatial resolution multispectral images. In this paper, Parrot’s small Multispectral (MS) camera Sequoia+ is used, and its images are evaluated at two archaeological sites, on the Byzantine wall (ground application) of Thessaloniki city (Greece) and on a mosaic floor (aerial application) at the archaeological site of Dion (Greece). The camera receives RGB and MS images simultaneously, a fact which does not allow image fusion to be performed, as in the standard utilization procedure of Panchromatic (PAN) and MS image of satellite passive systems. In this direction, that is, utilizing the image fusion processes of satellite PAN and MS images, this paper demonstrates that with proper digital processing the images (RGB and MS) of small MS cameras can lead to a fused image with a high spatial resolution, which retains a large percentage of the spectral information of the original MS image. The high percentage of spectral fidelity of the fused images makes it possible to perform high-precision digital measurements in archaeological sites such as the accurate digital separation of the objects, area measurements and retrieval of information not so visible with common RGB sensors via the MS and RGB data of small MS sensors.

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