A comparative study of target detection algorithms for hyperspectral imagery

2009 ◽  
Author(s):  
Xiaoying Jin ◽  
Scott Paswaters ◽  
Harold Cline
Keyword(s):  
Comparative Study ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Detection Algorithms

scholarly journals A comparative study of target detection algorithms in hyperspectral imagery applied to agricultural crops in Colombia

Tecnura ◽  
2016 ◽  
Vol 20 (49) ◽  
pp. 86 ◽  
Author(s):  
Ariolfo Camacho Velasco ◽  
César Augusto Vargas García ◽  
Henry Arguello Fuentes
Keyword(s):  
Comparative Study ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Agricultural Crops ◽  
Detection Algorithms

Contexto: Las imágenes hiperespectrales (HSI) contienen información en alta resolución espectral, en cientos de bandas contiguas sobre un rango del espectro electromagnético. Se ha tomado ventaja de la información espectral mediante algoritmos de clasificación, detección de cambios, de anomalías o detección de objetivos. Específicamente, en las dos últimas décadas han sido propuestos diferentes algoritmos para detectar objetivos en HSI. Sin embargo, encontrar un algoritmo de detección con un desempeño óptimo para diferentes escenarios y objetivos, aún es materia de investigación, debido a la alta variabilidad espectral y diversidad de escenarios del mundo real.Objetivo: La presente investigación realiza un estudio comparativo de algoritmos de detección de objetivos en imágenes hiperespectrales aplicados a la agricultura colombiana.Método: Las evaluaciones se realizaron sobre 20 HSI reales adquiridas por el sensor satelital Hyperion y 6 HSI sintéticas con diferentes niveles de ruido. En el desarrollo de la investigación se implantaron 5 objetivos sintéticos; y se extrajeron más de 115 firmas espectrales reales, 11 de ellas fueron usadas como objetivo en la evaluación de los algoritmos, permitiendo la caracterización de 5 cultivos agrícolas del nororiente colombiano en 5 áreas de estudio diferentes. Los resultados muestran que el algoritmo de Estimación de Coherencia Adaptativo (ACE) presenta un mejor desempeño con probabilidades de detección PD > 90% para diferentes escenarios y objetivos de tipo agrícola, tanto en imágenes sintéticas como reales.Conclusiones: En aplicaciones de detección de objetivos en HSI, es crítico encontrar un algoritmo que presente un rendimiento óptimo para diferentes escenarios y objetivos, debido a la variabilidad espectral generada por las diferentes condiciones geográficas de Colombia. Por otra parte, este trabajo permite mostrar que es posible realizar nuevas investigaciones y aplicaciones a nivel nacional tomando ventaja de las técnicas de imágenes hiperespectrales y algoritmos de detección espectral; específicamente en el sector de la agricultura colombiana.

Comparative Studies of Target Detection Algorithm for Hyperspectral Imagery

2015 ◽  
Vol 1 (1) ◽  
Author(s):  
B K Nagesha ◽  
M R Puttaswamy ◽  
Dsouza Hasmitha ◽  
G Hemantha Kumar
Keyword(s):  
Comparative Study ◽  
Hyperspectral Imaging ◽  
Target Detection ◽  
Spatial Resolution ◽  
Hyperspectral Image ◽  
Hyperspectral Imagery ◽  
Detection Algorithm ◽  
Hyperspectral Data ◽  
Detection Algorithms ◽  
Complex Process

<p>Target detection in hyperspectral imagery is a complex process due to many factors. Exploiting the hyperspectral image<br />for analysis is very challenging due to large information and low spatial resolution. However, hyperspectral target<br />detection has numerous applications. Hence, it is important to pursue research in target detection. In this paper, a<br />comparative study of target detection algorithms for hyperspectral imagery is presented along with scope for future<br />research. A comparative study behind the hyperspectral imaging is detailed. Also, various challenges involved in<br />exploring the hyperspectral data are discussed.</p>

scholarly journals Angle Distance-Based Hierarchical Background Separation Method for Hyperspectral Imagery Target Detection

2020 ◽  
Vol 12 (4) ◽  
pp. 697 ◽  
Author(s):  
Xiaohui Hao ◽  
Yiquan Wu ◽  
Peng Wang
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Original Data ◽  
Separation Method ◽  
Detection Accuracy ◽  
Target Information ◽  
Detection Algorithms ◽  
Target Spectrum ◽  
Candidate Target ◽  
The Given

Traditional detectors for hyperspectral imagery (HSI) target detection (TD) output the result after processing the HSI only once. However, using the prior target information only once is not sufficient, as it causes the inaccuracy of target extraction or the unclean separation of the background. In this paper, the target pixels are located by a hierarchical background separation method, which explores the relationship between the target and the background for making better use of the prior target information more than one time. In each layer, there is an angle distance (AD) between each pixel spectrum in HSI and the given prior target spectrum. The AD between the prior target spectrum and candidate target ones is smaller than that of the background pixels. The AD metric is utilized to adjust the values of pixels in each layer to gradually increase the separability of the background and the target. For making better discrimination, the AD is calculated through the whitened data rather than the original data. Besides, an elegant and ingenious smoothing processing operation is employed to mitigate the influence of spectral variability, which is beneficial for the detection accuracy. The experimental results of three real hyperspectral images show that the proposed method outperforms other classical and recently proposed HSI target detection algorithms.

Hyperspectral Blind Unmixing and Multiple Target Detection Using Linear Mixture Model

2012 ◽  
Vol 488-489 ◽  
pp. 1224-1228
Author(s):  
Qaisar Mushtaq ◽  
Ihsan Ul Haq ◽  
Muhammad Ahmad ◽  
Muhammad Sohaib
Keyword(s):  
Comparative Study ◽  
Blind Source Separation ◽  
Mixture Model ◽  
Target Detection ◽  
Hyperspectral Imagery ◽  
Component Analysis ◽  
Separation Technique ◽  
Multiple Target ◽  
Linear Mixture Model ◽  
Vertex Component Analysis

In this paper a blind source separation technique Joint Approximate Diagonalization of Eigen-matrices (JADE) is investigated to unmixing and multiple target detection for hyperspectral imagery data. Our targeted minerals are Alunite, Buddingtonite, Calcite and Kaolinite in ‘Cuprite’ scene data that has been widely used for research experiments in hyperspectral imagery. A comparative study is conducted to show the effectiveness of the JADE with Vertex Component Analysis. The results are evaluated with both full and reduced bands.

Evaluation of the GPU architecture for the implementation of target detection algorithms for hyperspectral imagery

2011 ◽  
Author(s):  
Blas Trigueros-Espinosa ◽  
Miguel Vélez-Reyes ◽  
Nayda G. Santiago-Santiago ◽  
Samuel Rosario-Torres
Keyword(s):  
Target Detection ◽  
Hyperspectral Imagery ◽  
Detection Algorithms ◽  
Gpu Architecture
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