Transductive and matched-pair machine learning for difficult target detection problems

2014 ◽  
Author(s):  
James Theiler
Keyword(s):  
Machine Learning ◽  
Target Detection ◽  
Matched Pair

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 Matched-Pair Machine Learning

Technometrics ◽  
2013 ◽  
Vol 55 (4) ◽  
pp. 536-547 ◽  
Author(s):  
James Theiler
Keyword(s):  
Machine Learning ◽  
Matched Pair

Target Detection using Radar Processors based on Machine Learning

2020 ◽  
Author(s):  
Enrique V. Carrera ◽  
Fernando Lara ◽  
Marcelo Ortiz ◽  
Alexis Tinoco ◽  
Ruben Leon
Keyword(s):  
Machine Learning ◽  
Target Detection
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