Hyperspectral Airborne 'Viareggio 2013 Trial' Data Collection for Detection Algorithm Assessment
| Autor: | Stefania Matteoli, Nicola Acito, Giovanni Corsini, Alessandro Rossi, Marco Diani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Atmospheric Science
Computer science hyperspectral imaging 0211 other engineering and technologies 02 engineering and technology computer.software_genre anomaly detection (AD) remote sensing anomalous change detection 0202 electrical engineering electronic engineering information engineering Computers in Earth Sciences 021101 geological & geomatics engineering free hyperspectral imagery Data collection target detection Hyperspectral imaging 020206 networking & telecommunications Benchmarking Object detection anomaly detection Data sharing Data set Anomalous change detection (ACD) spectral signatures experimental data Anomaly detection Data mining Anomalous change detection (ACD) anomaly detection (AD) free hyperspectral imagery spectral signatures target detection Algorithm computer Change detection |
| Zdroj: | IEEE journal of selected topics in applied earth observations and remote sensing 9 (2016): 2365–2376. doi:10.1109/JSTARS.2016.2531747 info:cnr-pdr/source/autori:Acito N.; Matteoli S.; Rossi A.; Diani M.; Corsini G./titolo:Hyperspectral Airborne 'Viareggio 2013 Trial' Data Collection for Detection Algorithm Assessment/doi:10.1109%2FJSTARS.2016.2531747/rivista:IEEE journal of selected topics in applied earth observations and remote sensing (Print)/anno:2016/pagina_da:2365/pagina_a:2376/intervallo_pagine:2365–2376/volume:9 |
| Popis: | For many years, the entire target detection scientific community has felt the urge for fully ground-truthed hyperspectral imagery data sets expressly released for testing and comparing detection algorithms. Although a few excellent data-sharing efforts have been carried out in the last decade, the use of either restricted or not well ground-truthed imagery still remains a common practice in the target detection literature. In this paper, we provide an overview of a new hyperspectral data set that we release to the scientific community with the specific goal of fostering unbiased comparison and scientific discussions of anomaly detection (AD), object detection, and anomalous change detection (ACD) algorithms. The data set is fully ground-truthed and documented and includes scenarios and experiments specifically conceived for detection algorithm comparison and benchmarking. Insights about the various possible data exploitation tasks are provided by making reference to noise estimation and reduction, AD, spectral signature-based target detection (SSBTD), and ACD. Experimental results concerning ACD and SSBTD are presented and highlight the usefulness of this new data set from the data sharing and algorithmic comparison perspectives. |
| Databáze: | OpenAIRE |
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