Application of unsupervised end member detection algorithms for spectral unmixing of hyperspectral data for mangrove species discrimination

2012 ◽  
Author(s):  
Somdatta Chakravortty ◽  
Arpita Saha Choudhury
Keyword(s):  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Species Discrimination ◽  
Mangrove Species ◽  
Detection Algorithms

scholarly journals GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

2020 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Luoma Wan ◽  
Yinyi Lin ◽  
Hongsheng Zhang ◽  
Feng Wang ◽  
Mingfeng Liu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Spectral Resolution ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Species Discrimination ◽  
Infrared Range ◽  
Landsat 8 ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Species Mapping

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

Tropical mangrove species discrimination using hyperspectral data: A laboratory study

2005 ◽  
Vol 65 (1-2) ◽  
pp. 371-379 ◽  
Author(s):  
Chaichoke Vaiphasa ◽  
Suwit Ongsomwang ◽  
Tanasak Vaiphasa ◽  
Andrew K. Skidmore
Keyword(s):  
Laboratory Study ◽  
Hyperspectral Data ◽  
Species Discrimination ◽  
Mangrove Species

scholarly journals DIFFERENT STATISTICAL METHODS FOR THE DISCRIMINATION OF TROPICAL MANGROVE SPECIES USING IN-SITU HYPERSPECTRAL DATA

2019 ◽  
Vol 9 (2) ◽  
pp. 49
Author(s):  
Tanumi Kumar ◽  
Dibyendu Dutta ◽  
Diya Chatterjee ◽  
K Chandrasekar ◽  
Goru Srinivasa Rao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Region ◽  
Hyperspectral Data ◽  
Feature Reduction ◽  
Reflectance Spectra ◽  
Stepwise Discriminant Analysis ◽  
Species Discrimination ◽  
Mangrove Species ◽  
Domain Specific ◽  
Continuum Removal

The study highlights the hyperspectral characteristics of canopies of 14 tropical mangrove species, belonging to nine families found in the tidal forests of the Indian Sundarbans. Hyperspectral observations were recorded using a field spectroradiometer, pre-processed and subjected to derivative analysis and continuum removal. Mann-Whitney U tests were applied on the spectral data in four spectral forms: (i) Reflectance Spectra (ii) First Derivative, (iii) Second Derivative and (iv) Continuum Removal Reflectance Spectra. Factor analysis was applied in each of the spectral forms for feature reduction and identification of the important wavelengths for species discrimination. Stepwise discriminant analysis was used on the feature reduced reflectance spectra to obtain optimal bands for computation of Jeffries–Matusita distance. The Mann-Whitney U test could be satisfactorily used for determining the significant (separable) bands for discriminating the species. In general, the red region, red edge domain, specific near infrared bands (including 759, 919, 934, 940, 948, 1152, 1156, 1159 and 1212 nm) and shortwave infrared region (1503–1766 nm) played major roles in spectral separability. Overall, hyperspectral data showed potential for discriminating between mangrove canopies of different species and the results of the study also indicated the usefulness of the applied statistical tools for discrimination.

scholarly journals Mangrove Species Discrimination and Health Assessment using AVIRIS-NG Hyperspectral Data

2019 ◽  
Vol 116 (7) ◽  
pp. 1136 ◽  
Author(s):  
Nilima R. Chaube ◽  
Nikhil Lele ◽  
Arundhati Misra ◽  
T. V. R. Murthy ◽  
Sudip Manna ◽  
...  
Keyword(s):  
Health Assessment ◽  
Hyperspectral Data ◽  
Species Discrimination ◽  
Mangrove Species

A Euclidean Distance based Super Resolution Method for Sub pixel target detection in Hyper Spectral Images

GIS Business ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 104-124
Author(s):  
Dr. K. C. Tiwari ◽  
Amrita Bhandari
Keyword(s):  
Spatial Distribution ◽  
Target Detection ◽  
Euclidean Distance ◽  
Super Resolution ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Resolution Method ◽  
Mixed Pixel ◽  
Detection Algorithms ◽  
Hyper Spectral

Most target detection algorithms suffer from the limitation that they can detect only the full pixels of the target while the target may also reside, besides the full pixel, partially in several surrounding pixels. In some cases, the target may even be embedded completely within the pixel. Both these cases are known as subpixel target detection problem. Many target detection applications, however, require detection of full pixels as well as detection of subpixel targets in the surrounding pixels which constitute a case of the mixed pixel. The problem is addressed by full pixel detection followed by spectral unmixing to determine the abundance fraction of the target. Though spectral unmixing gives the abundance fractions, it still does not give the spatial distribution/ arrangement of subpixels of the target with the surrounding pixels. The process of optimizing the spatial distribution of subpixels inside any given pixel based on the available abundance fractions is known as super resolution. This paper investigates Inverse Euclidean distance based super resolution. The algorithm performs well at different scale factors both for synthetic and real hyperspectral data which can aid the super resolution process significantly and thereby enhance the identification and recognition of target. A comparative assessment is also performed with Pixel Swap algorithm.

scholarly journals Hyperspectral Data Simulation (Sentinel-2 to AVIRIS-NG) for Improved Wildfire Fuel Mapping, Boreal Alaska

2021 ◽  
Vol 13 (9) ◽  
pp. 1693
Author(s):  
Anushree Badola ◽  
Santosh K. Panda ◽  
Dar A. Roberts ◽  
Christine F. Waigl ◽  
Uma S. Bhatt ◽  
...  
Keyword(s):  
Land Cover ◽  
Spectral Resolution ◽  
Hyperspectral Image ◽  
Spectral Response ◽  
Spectral Characteristics ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
High Spectral Resolution ◽  
Fuel Mapping ◽  
Sentinel 2

Alaska has witnessed a significant increase in wildfire events in recent decades that have been linked to drier and warmer summers. Forest fuel maps play a vital role in wildfire management and risk assessment. Freely available multispectral datasets are widely used for land use and land cover mapping, but they have limited utility for fuel mapping due to their coarse spectral resolution. Hyperspectral datasets have a high spectral resolution, ideal for detailed fuel mapping, but they are limited and expensive to acquire. This study simulates hyperspectral data from Sentinel-2 multispectral data using the spectral response function of the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor, and normalized ground spectra of gravel, birch, and spruce. We used the Uniform Pattern Decomposition Method (UPDM) for spectral unmixing, which is a sensor-independent method, where each pixel is expressed as the linear sum of standard reference spectra. The simulated hyperspectral data have spectral characteristics of AVIRIS-NG and the reflectance properties of Sentinel-2 data. We validated the simulated spectra by visually and statistically comparing it with real AVIRIS-NG data. We observed a high correlation between the spectra of tree classes collected from AVIRIS-NG and simulated hyperspectral data. Upon performing species level classification, we achieved a classification accuracy of 89% for the simulated hyperspectral data, which is better than the accuracy of Sentinel-2 data (77.8%). We generated a fuel map from the simulated hyperspectral image using the Random Forest classifier. Our study demonstrated that low-cost and high-quality hyperspectral data can be generated from Sentinel-2 data using UPDM for improved land cover and vegetation mapping in the boreal forest.

Algoritmo de Descomposición Ciega Basado en el Modelo de Mezcla Multi-Lineal

2021 ◽  
Author(s):  
◽  
J. N. Mendoza Chavarría
Keyword(s):  
State Of The Art ◽  
Hyperspectral Data ◽  
Spectral Unmixing ◽  
Mixing Models ◽  
Nonlinear Interactions ◽  
Estimation Errors ◽  
Spectral Signatures ◽  
Scattering Effects ◽  
Linear Unmixing ◽  
Multiple Light Scattering

Spectral unmixing has proven to be a great tool for the analysis of hyperspectral data, with linear mixing models (LMMs) being the most used in the literature. Nevertheless, due to the limitations of the LMMs to accurately describe the multiple light scattering effects in multi and hyperspectral imaging, new mixing models have emerged to describe nonlinear interactions. In this paper, we propose a new nonlinear unmixing algorithm based on a multilinear mixture model called Non-linear Extended Blind Endmember and Abundance Extraction (NEBEAE), which is based on a linear unmixing method established in the literature. The results of this study show that proposed method decreases the estimation errors of the spectral signatures and abundance maps, as well as the execution time with respect the state of the art methods.

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