scholarly journals Endmember Estimation with Maximum Distance Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 713
Author(s):  
Xuanwen Tao ◽  
Mercedes E. Paoletti ◽  
Juan M. Haut ◽  
Peng Ren ◽  
Javier Plaza ◽  
...  
Keyword(s):  
Hyperspectral Image ◽  
Real Data ◽  
Estimation Algorithm ◽  
Maximum Distance ◽  
Spectral Space ◽  
Endmember Extraction ◽  
Affine Hull ◽  
Spectral Signatures ◽  
Distance Analysis ◽  
Coordinate Origin

Endmember estimation plays a key role in hyperspectral image unmixing, often requiring an estimation of the number of endmembers and extracting endmembers. However, most of the existing extraction algorithms require prior knowledge regarding the number of endmembers, being a critical process during unmixing. To bridge this, a new maximum distance analysis (MDA) method is proposed that simultaneously estimates the number and spectral signatures of endmembers without any prior information on the experimental data containing pure pixel spectral signatures and no noise, being based on the assumption that endmembers form a simplex with the greatest volume over all pixel combinations. The simplex includes the farthest pixel point from the coordinate origin in the spectral space, which implies that: (1) the farthest pixel point from any other pixel point must be an endmember, (2) the farthest pixel point from any line must be an endmember, and (3) the farthest pixel point from any plane (or affine hull) must be an endmember. Under this scenario, the farthest pixel point from the coordinate origin is the first endmember, being used to create the aforementioned point, line, plane, and affine hull. The remaining endmembers are extracted by repetitively searching for the pixel points that satisfy the above three assumptions. In addition to behaving as an endmember estimation algorithm by itself, the MDA method can co-operate with existing endmember extraction techniques without the pure pixel assumption via generalizing them into more effective schemes. The conducted experiments validate the effectiveness and efficiency of our method on synthetic and real data.

scholarly journals Backtracking-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data

2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Fanqiang Kong ◽  
Wenjun Guo ◽  
Yunsong Li ◽  
Qiu Shen ◽  
Xin Liu
Keyword(s):  
Hyperspectral Image ◽  
Matching Pursuit ◽  
Real Data ◽  
Hyperspectral Data ◽  
Orthogonal Matching Pursuit ◽  
Actual Number ◽  
Spectral Library ◽  
Spectral Signatures ◽  
Optimal Subset ◽  
Negative Effect

Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed signatures of a hyperspectral image can be expressed in the form of linear combination of only a few spectral signatures (endmembers) in an available spectral library. Simultaneous orthogonal matching pursuit (SOMP) algorithm is a typical simultaneous greedy algorithm for sparse unmixing, which involves finding the optimal subset of signatures for the observed data from a spectral library. But the numbers of endmembers selected by SOMP are still larger than the actual number, and the nonexisting endmembers will have a negative effect on the estimation of the abundances corresponding to the actual endmembers. This paper presents a variant of SOMP, termed backtracking-based SOMP (BSOMP), for sparse unmixing of hyperspectral data. As an extension of SOMP, BSOMP incorporates a backtracking technique to detect the previous chosen endmembers’ reliability and then deletes the unreliable endmembers. Through this modification, BSOMP can select the true endmembers more accurately than SOMP. Experimental results on both simulated and real data demonstrate the effectiveness of the proposed algorithm.

scholarly journals Multiobjective Optimized Endmember Extraction for Hyperspectral Image

2017 ◽  
Vol 9 (6) ◽  
pp. 558 ◽  
Author(s):  
Rong Liu ◽  
Bo Du ◽  
Liangpei Zhang
Keyword(s):  
Hyperspectral Image ◽  
Endmember Extraction

scholarly journals HONEY CROP ESTIMATION FROM SPACE: DETECTION OF LARGE FLOWERING EVENTS IN WESTERN AUSTRALIAN FORESTS

2018 ◽  
Vol XLII-1 ◽  
pp. 79-86
Author(s):  
T. Campbell ◽  
P. Fearns
Keyword(s):  
Remotely Sensed ◽  
Spectral Space ◽  
Remotely Sensed Data ◽  
Response Curves ◽  
Real World Data ◽  
Distance Analysis ◽  
Peak Separation ◽  
Flower Density ◽  
Harvest Data ◽  
Detailed Assessment

<p><strong>Abstract.</strong> Recent studies have shown that in the spectral space there is often a better spectral separation between leaves and flowers and even between flowers of different species than between leaves of different species. In this study we assess the ability of satellite remotely sensed data to detect the flowering of Red Gum trees (<i>Corymbia calophylla</i>) in Western Australia, the state’s largest annual honey crop. Spectroradiometer measurements of flowers, leaves and groundcover from Red Gum forests were subjected to ANOVA analysis, which showed that flowers are spectrally different to their environment for 92<span class="thinspace"></span>% of the wavelengths between 350<span class="thinspace"></span>nm and 1800<span class="thinspace"></span>nm. A more detailed assessment, using the JM Distance calculation, showed that the spectra can be reliably separated using 10<span class="thinspace"></span>% of the wavelengths, with peak separation between 518<span class="thinspace"></span>nm and 557<span class="thinspace"></span>nm. To assess the ability of satellite-borne sensors to detect the presence of flowers, the spectroradiometer data were convolved with satellite instruments’ response curves to create synthetic remotely sensed datasets on which JM Distance analysis was performed. MODIS blue bands achieved a median JM Distance of greater than 1.9 and therefore should be able to detect the presence of flowers from the environment. Further assessment showed that the shortest wavelength bands for MODIS, VIIRS and Sentinel 3 all occur where the flower spectra have lower reflectance than their natural background. A sensitivity analysis of percentage flower cover for a pixel showed that the highest sensitivity was obtained by dividing the band closest to 520<span class="thinspace"></span>nm by the shortest wavelength band for data from these three sources. The MODIS band 10/band 8 metric was tested for its ability to detect flowers in real-world data using 15 years of qualitative honey harvest data from one apiary site as a proxy for flower density. This test was successful as, while there was some overlap between good, moderate and poor years, the poor years could be separated from the other years with nearly 80<span class="thinspace"></span>% accuracy.</p>

scholarly journals A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

2020 ◽  
Vol 12 (18) ◽  
pp. 2923
Author(s):  
Tengfei Zhou ◽  
Xiaojun Cheng ◽  
Peng Lin ◽  
Zhenlun Wu ◽  
Ensheng Liu
Keyword(s):  
Point Cloud ◽  
A Priori ◽  
Real Data ◽  
Point Clouds ◽  
Estimation Algorithm ◽  
General Point ◽  
Exterior Orientation ◽  
Self Calibration ◽  
Laser Scanners ◽  
Orientation Parameters

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.

scholarly journals A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

2021 ◽  
Author(s):  
Bataa Lkhagvasuren ◽  
Minkyu Kwak ◽  
Hong Sung Jin ◽  
Gyuwon Seo ◽  
Sungyool Bong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Real Data ◽  
Estimation Algorithm ◽  
Open Circuit ◽  
State Of Charge ◽  
Least Square ◽  
State Equations ◽  
Sigma Point ◽  
Parameter Dependent

<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

scholarly journals A variable-length window-wise parameter-dependent state of charge estimation by Kalman filters

2021 ◽  
Author(s):  
Bataa Lkhagvasuren ◽  
Minkyu Kwak ◽  
Hong Sung Jin ◽  
Gyuwon Seo ◽  
Sungyool Bong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Kalman Filters ◽  
Real Data ◽  
Estimation Algorithm ◽  
Open Circuit ◽  
State Of Charge ◽  
Least Square ◽  
State Equations ◽  
Sigma Point ◽  
Parameter Dependent

<div>This paper proposes a new window-wise state of charge (SOC) estimation algorithm based on Kalman filters (KF). In the first stage, the equivalent circuit model's parameters are estimated by a least square estimation window-wise, assuming a linear SOC and open-circuit voltage (OCV) relation. The algorithm accurately estimates the parameters and observes the changes that depend on SOC. Moreover, based on the estimated parameters, the OCV values are identified. In the next stage, window-wise linear Kalman filter(ES-LKF) without hysteresis and extended Kalman filter (ES-EKF) and sigma-point Kalman filter (ES-SPKF) algorithm with hysteresis are executed to estimate SOC. Having fewer state equations and hysteresis parameters tuned up in an off-line way, the ES-EKF and ES-SPKF perform better than the algorithms considered in previous works. The algorithms are validated by experiments with real data obtained from lab tests.</div>

scholarly journals JL-GFDN: A Novel Gabor Filter-Based Deep Network Using Joint Spectral-Spatial Local Binary Pattern for Hyperspectral Image Classification

2020 ◽  
Vol 12 (12) ◽  
pp. 2016 ◽  
Author(s):  
Tao Zhang ◽  
Puzhao Zhang ◽  
Weilin Zhong ◽  
Zhen Yang ◽  
Fan Yang
Keyword(s):  
Local Binary Pattern ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Spectral Characteristics ◽  
Three Dimensional ◽  
Real Data ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Deep Network

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.

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