Hyperspectral data collection for the assessment of target detection algorithms: the Viareggio 2013 trial

2014 ◽  
Author(s):  
Alessandro Rossi ◽  
Nicola Acito ◽  
Marco Diani ◽  
Giovanni Corsini ◽  
Sergio Ugo De Ceglie ◽  
...  
Keyword(s):  
Data Collection ◽  
Target Detection ◽  
Hyperspectral Data ◽  
Detection Algorithms

Comparative Assessment of Target-Detection Algorithms for Urban Targets Using Hyperspectral Data

2021 ◽  
Vol 87 (5) ◽  
pp. 349-362
Author(s):  
Shalini Gakhar ◽  
K.C. Tiwari
Keyword(s):  
Machine Learning ◽  
Target Detection ◽  
City Planning ◽  
Urban Landscape ◽  
Relevant Information ◽  
Hyperspectral Data ◽  
Machine Learning Algorithms ◽  
Distance Measures ◽  
Information Measures ◽  
Detection Algorithms

Hyperspectral data present better opportunities to exploit the treasure of spectral and spatial content that lies within their spectral bands. Hyperspectral data are increasingly being considered for exploring levels of urbanization, due to their capability to capture the spectral variability that a modern urban landscape offers. Data and algorithms are two sides of a coin: while the data capture the variations, the algorithms provide suitable methods to extract relevant information. The literature reports a variety of algorithms for extraction of urban information from any given data, with varying accuracies. This article aims to explore the binary-classifier approach to target detection to extract certain features. Roads and roofs are the most common features present in any urban scene. These experiments were conducted on a subset of AVIRIS-NG hyperspectral data from the Udaipur region of India, with roads and roofs as targets. Four categories of target-detection algorithms are identified from a literature survey and our previous experience—distance measures, angle-based measures, information measures, and machine-learning measures—followed by performance evaluation. The article also presents a brief taxonomy of algorithms; explores methods such as the Mahalanobis angle, which has been reported to be effective for extraction of urban targets; and explores newer machine-learning algorithms to increase accuracy. This work is likely to aid in city planning, sustainable development, and various other governmental and nongovernmental efforts related to urbanization.

scholarly journals Ensemble-Based Cascaded Constrained Energy Minimization for Hyperspectral Target Detection

2019 ◽  
Vol 11 (11) ◽  
pp. 1310 ◽  
Author(s):  
Rui Zhao ◽  
Zhenwei Shi ◽  
Zhengxia Zou ◽  
Zhou Zhang
Keyword(s):  
Target Detection ◽  
Energy Minimization ◽  
Hyperspectral Image ◽  
Hyperspectral Data ◽  
Least Square ◽  
Learning System ◽  
Strong Nonlinearity ◽  
Generalization Ability ◽  
Detection Algorithms ◽  
Ensemble Techniques

Ensemble learning is an important group of machine learning techniques that aim to enhance the nonlinearity and generalization ability of a learning system by aggregating multiple learners. We found that ensemble techniques show great potential for improving the performance of traditional hyperspectral target detection algorithms, while at present, there are few previous works have been done on this topic. To this end, we propose an Ensemble based Constrained Energy Minimization (E-CEM) detector for hyperspectral image target detection. Classical hyperspectral image target detection algorithms like Constrained Energy Minimization (CEM), matched filter (MF) and adaptive coherence/cosine estimator (ACE) are usually designed based on constrained least square regression methods or hypothesis testing methods with Gaussian distribution assumption. However, remote sensing hyperspectral data captured in a real-world environment usually shows strong nonlinearity and non-Gaussianity, which will lead to performance degradation of these classical detection algorithms. Although some hierarchical detection models are able to learn strong nonlinear discrimination of spectral data, due to the spectrum changes, these models usually suffer from the instability in detection tasks. The proposed E-CEM is designed based on the classical CEM detection algorithm. To improve both of the detection nonlinearity and generalization ability, the strategies of “cascaded detection”, “random averaging” and “multi-scale scanning” are specifically designed. Experiments on one synthetic hyperspectral image and two real hyperspectral images demonstrate the effectiveness of our method. E-CEM outperforms the traditional CEM detector and other state-of-the-art detection algorithms. Our code will be made publicly available.

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.

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 Assessment of target detection limits in hyperspectral data

2015 ◽  
Author(s):  
W. Gross ◽  
J. Boehler ◽  
H. Schilling ◽  
W. Middelmann ◽  
J. Weyermann ◽  
...  
Keyword(s):  
Target Detection ◽  
Detection Limits ◽  
Hyperspectral Data
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