Processing of Hyperspectral Imagery for Contamination Detection in Urban Areas

1. Processing of Hyperspectral Imagery for Contamination Detection in Urban Areas Mikhail A. Popov, Sergey A. Stankevich, Ludmila P. Lischenko Scientific Centre for Aerospace Research of the Earth 55-B Oles Gonchar st., Kiev, 01601, Ukraine, +38 (044) 482 01 66, mpopov@casre.kiev.ua Vladimir V. Lukin, Nikolay N. Ponomarenko National Aerospace University 17 Chkalov st., Kharkov, 61070, Ukraine, +38 (057) 707 48 41, lukin@ai.kharkov.ua The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

2. Scientific Centre for Aerospace Research of the Earth Geochemical contamination map of Kiev The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

3. Scientific Centre for Aerospace Research of the Earth D(λ) 4r2(λ) C(λ) = log2[1+ψ(λ)] Hyperspectral imagery informativity D(λ) – Kullback-Leibler divergence, r(λ) – equivalent spatial resolution , ψ(λ) – equivalent SNR Optimal bands selection λ* : C(λ*) → max Pseudogradient search λ0 := 1T λi := λi-1+ ∆λ grad C(λi-1) grad C(λ*) ≤ 0 The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

4. Processing dataflow Scientific Centre for Aerospace Research of the Earth From airborne / speceborne imaging systems T o u s e r s a n d c u s t o m e r s The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

5. Hyperspectral imagery classification Scientific Centre for Aerospace Research of the Earth ∂ρ(λ) ∂λ P(ρ) = Pr ∫|sign[∆ρ(λ), ε(λ)]| dλ + ∫|sign[∆ , ε(λ)]| dλ P(ρ) – spectral-topological metric, ∆ρ(λ) – spectral difference, ε(λ) – statistical confidence Bayesian decision rule P(x|A) P(x|A)+P(x|B) if x “open natural lands” 0 otherwise P(x) = P(x) – contamination level, P(x|A) – conditional probability of membership of the x pixel signal in contaminated site reference sample A, P(x|B) – conditional probability of membership of the x pixel signal in non-contaminated area reference sample B The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

6. Classification software Scientific Centre for Aerospace Research of the Earth ClassGeo-M hyperspectral imagery spectral-topological classification software Project/ROI module Supervised classification module The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

7. Geochemical contaminations mapping Scientific Centre for Aerospace Research of the Earth abcd a – EO-1/Hyperion hyperspectral satellite image, September 1, 2002, spectral bands 115 (1296 nm), 95 (1094 nm), 36 (712 nm), 30 m spatial resolution; b – land cover classification; c – open surfaces separation using OBIA between open artificial areas and open natural lands; d – contaminations spatial distribution for an “open natural lands” class The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

8. Contamination data validation Scientific Centre for Aerospace Research of the Earth The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

9. Hyperspectral data compression Scientific Centre for Aerospace Research of the Earth abc EO-1/Hyperion spectral bands 14 (488 nm), 22 (569 nm), 26 (610 nm), 32 (671 nm)) before (a) and after (b) 9.6 times lossy compression; c – the pixel deviations histogram of the compressed image The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010

10. References Scientific Centre for Aerospace Research of the Earth • Popov M A, Stankevich S A, Lischenko L P, Podorvan V N (2007) Mapping of technogenic contaminations of urban area using hyperspectral imagery. Thesis of Polish-Ukrainian Workshop on Space Applications, Warsaw (Poland) • Stankevich S A (2006) Quantitative estimation of hyperspectral aerospace imagery informativity for the remote sensing thematic tasks (Ukrainian). Proceedings of NAS of Ukraine, 10:136-139 • Stankevich S A (2007) Hyperspectral aerospace imagery spectral bands optimal selection for the remote sensing thematic tasks (Russian). Space Science & Technology, 13(2):25-28 • Lukin V V (2009) Processing of multichannel RS data for environment monitoring. Proceedings of NATO Advanced Research Workshop on Geographical Information Processing and Visual Analytics for Environmental Security, Trento (Italy) • Lukin V V, Ponomarenko N N, Zelensky A A, Kurekin A A, Lever K (2008) Compression and classification of noisy multichannel remote sensing images. Proceedings of XIV SPIE Conference on Image and Signal Processing for Remote Sensing, Cardiff (UK) • Lukin V V, Ponomarenko N N, Zriakhov M S, Krivenko S S, Zelensky A A, Popov M A, Stankevich S A, Kovalchuk S P, Titarenko O V (2009) Methods for remote sensing hyperspectral imagery processing and compression (Russian). Proceedings of 8th International Conference “Modern Information Technologies in Ecological Security, Natural Resources and Disasters Management”, Rybachie (Ukraine) • Ponomarenko N N, Krivenko S S, Lukin V V, Egiazarian K O (2009) Visual quality of lossy compressed images. Proceedings of 10th International Conference “The Experience of Designing and Application of CAD Systems in Microelectronics” CADSM’2009, Svalyava (Ukraine) • Ponomarenko N N, Lukin V V, Egiazarian K O, Astola J T (2010) A method for blind estimation of spatially correlated noise characteristics. Proceedings of SPIE, 7532:3208 The Fourteenth Conference of software users of ESRI in Ukraine, Yalta, 24-28 May, 2010