**5. Marine algorithms**

512 Remote Sensing – Applications

Fig. 17. Examples of environmental (H2O, NaCl) and chemicals (NH3) stresses on plants.

Fig. 18. Representative spectra for canola experiments using Cl2 and SO2. Mean spectra of control G1 (black line, mature healthy) and G4C (pink line, high senescence) are included for

environmental stresses result in diagnostic light reflectance data trends from healthy mature

Comparison of relevant vegetation indices, such as that depicted in Figure 19, showed that specific combinations could be used to distinguish NH3, SO2, Cl2 consistently across all three species (Rogge et al, 2008). The trends result from the variable leaf response within plants, between plants and between species and it is expected much of the variability observed within species would be preserved or even enhanced in nature. As such it is encouraging for the possible detection of TIC effects on natural vegetation using airborne/spaceborne imagery.

As the detection methodology was developed from leaf-level observations, it is important to note that field trials remain to be conducted in order to test if the findings of this study can be extended to the detection of TICs in the natural environment. The principal unknown is the effect of varying vegetation canopy structural parameters (e.g. canopy gaps, leaf area) and background properties (litter and soil reflectance) on the specific data trends that were

reference. Arrows denote key absorption features observed in endmember spectra compared with G1. Black dotted box denotes smoothing of red edge. Chemical exposure

differences is apparent.

to highly stressed leaves.

identified.

The HYMEX project studied several potential marine applications in collaboration with Borstad Associates and the Dept. of Fisheries and Ocean (Institut Maurice Lamontagne and Bedford Institute of Oceanography). We conducted airborne hyperspectral surveys on East and West coasts of Canada to evaluate algorithms for near-shore bathymetry, beach trafficability, near-shore bottom type mapping as well as retrieval of chlorophyll and suspended matter concentrations as indicator of water clarity. More details are provided in (Ardouin, 2007). Through this work it was realized that most of these algorithms can be applied to multispectral imagery and that their experimental validation is difficult. The later is particularly true for products that vary with time (or current) and thus would require many measurement stations (for validation) that would need to operate coincidentally with the airborne survey and be distributed over the area of the survey.

More recently, we tasked OEA Technologies to provide an operational assessment of HYMEX marine algorithms. In this assessment, a distinction was made between dynamic (e.g. water color) and static (e.g. bathymetry) products. It was pointed out that the Canadian Forces needs for off-shore dynamic products (e.g. water colour) is already fulfilled by marine multispectral sensors (MERIS, MODIS) with pixel size > 250m (Williams, 2009). There might however be a niche for hyperspectral sensors (airborne and spaceborne) which

Demonstration of Hyperspectral Image Exploitation for Military Applications 515

Buckingham, R.; Staenz, K. & Hollinger (2002) A., Review of Canadian Airborne and Space

Buckingham, R. & Staenz, K. (2008) Review of current and planned civilian space

Cooley T.; Davis, M. & Straight, S. (2006) ARTEMIS-Advanced Tactically-Effective Military

Davenport, M. & Ressl, W. (1999) Shadow Removal in Hyperspectral Imagery, *International Symposium on Spectral Sensing Research (ISSSR 99)*, Las Vegas (November 1999) Deyholos, M.; Faust, A.A.; Miao, M.; Montoya, R. & Donahue, D.A. (2006) Feasibility of

John H. Holloway, Jr, eds., *Proc. SPIE,* Vol. 6217, (May 2006), pp. 700-711 Deyholos, M.K.; Rogge, D.; Rivard, B. & Faust, AA. (2007) Plants as sensors for toxic

Deyholos, M. (2009) *Demonstration of Plant-based Explosives Detection*, Contract Report Defence R&D Canada – Suffield, DRDC Suffield CR-2010-010 (2009). Pearlman, J.S.; Barry, P.S; Segal, C.C.; Shepanski, J.; Beiso & D. & Carman, S.L. (2003)

*Remote Sensing*, Vol. 41, No. 6, (June 2003), pp. 1160- 1173, ISSN 0196-2892 Peddle, D.R. & Ferguson, D.T. (2002) Optimization of Multisource Data Analysis using

Peddle, D.R.; Franklin, S.E.; Johnson, R.L.; Lavigne, M.B. & Wulder, M.A. (2003) Structural

Peddle, D.R. & Smith, A.M. (2005) Spectral Mixture Analysis of Agricultural Crops:

Peddle, D.R.; Boulton, R.B.; Pilger, N.; Bergeron, M. & Hollinger, A. (2008) Hyperspectral

mission, *Can. J. Remote Sensing*, Vol. 34, Suppl. 1, (2008), pp. S198-S216 Reed, I. S. & Yu, X. (1990) Adaptive multiple-band CFAR detection of an optical pattern

*Processing*, Vol. 38, No. 10, (October 1990), pp. 1760-1770, ISSN 0096-3518 Rivard, B.; Deyholos, M. & Rogge, D. (2008) *Chemical Effects on Vegetation Detectable in optical* 

*Sensing* Vol. 41, No. 1, (Jan 2003), pp. 163-166, ISSN 0196-2892

*Journal of Remote Sensing*, Vol. 26, No. 22, (2005), pp. 4959–4979

Vol. 48, No. 1, (2002), pp. 115-121

*Spectrometry IX*. Vol. 5159, (2003) pp. 392-405

*Developmental Biology-Animal*, Vol. 43, pp. S7-S7.

No 1, (2002), pp. 45-52

Suffield CR-2008-234, (2008)

S187-S197

Activities in Hyperspcetral Remote Sensing, *Canadian Aeronautics and Space Journal*,

hyperspectral sensors for EO, *Can. J. Remote Sensing*, Vol. 34, Suppl. 1, (2008), pp.

Imaging Spectrometer: Tactical Satellite 3 for Responsive Space Missions, *International Symposium on Spectral Sensing Research*, Bar Harbor, MN, (May 2006) Cutter, M.A.; Johns, L.S.; Lobb, D.R.; Williams, T.L. & Settle, J.J. (2003) Flight Experience of

the Compact High Resolution Imaging Spectrometer (CHRIS), *Proc. of SPIE Imaging* 

landmine detection using transgenic plants, in *Detection and Remediation Technologies for Mines and Minelike Targets XI*, J. Thomas Broach, Russell S. Harmon,

industrial chemicals and munitions: A feasibility analysis, *In Vitro Cellular &* 

Hyperion, a Space-Based Imaging Spectrometer, *IEEE Trans. on Geoscience and* 

Evidential Reasoning for GIS Data Classification, *Computers & Geosciences*, Vol. 28,

Change Detection in a Disturbed Conifer Forest Using a Geometric Optical Reflectance Model in Multiple-Forward Mode. *IEEE Trans. on Geoscience and Remote* 

Endmember Validation and Biophysical Estimation in Potato Plots, *International* 

detection of chemical vegetation stress: evaluation for the Canadian HERO satellite

with unknown spectral distribution, *IEEE Trans. on Acoustics, Speech, and Signal* 

*bands 350-2500 nm*, Contract Report Defence R&D Canada - Suffield , DRDC

typically have better spatial resolution (e.g. from submeter to tens of meter) for near-shore static and dynamic products. The better spatial resolution and increased number of bands of hyperspectral sensors might provide an ability to handle the more complex near-shore environment. Potential static products to consider include target detection and near-shore bottom characterization in support of mine countermeasures and battlespace mapping and possibly submarine operations. To this we can also add near-shore bathymetry in support of route survey, battlespace mapping, anti-submarine warfare and submarine operations. While not requiring hyperspectral sensing, HSI could keep playing a role (e.g. selection of optimal bands) in the development of new dynamic products for both near-shore and offshore applications. Overall, this assessment point to possible follow-up for marine applications development with hyperspectral sensors.

#### **6. Conclusion**

In this chapter, we discussed a wide variety of military applications resulting from the exploitation of reflective hyperspectral imagery. These applications were demonstrated in the DRDC HYMEX project, allowing DRDC and the Canadian Forces stakeholders to get more familiar with the military utility of hyperspectral imagery. While some of these applications such as target detection are relatively mature and are near to operational deployment, others still require further development but are representative of the unique capability of hyperspectral remote sensing. The many datasets that were acquired and the algorithms and exploitation tools that were developed in the project are being used to continue the development of hyperspectral technology at DRDC. One avenue that is being pursued is the development of an airborne hyperspectral real-time target detection demonstration system. We are also looking at opportunities to further develop the land mapping and marine applications areas as well as potential space-based demonstration with international partners.

#### **7. References**


typically have better spatial resolution (e.g. from submeter to tens of meter) for near-shore static and dynamic products. The better spatial resolution and increased number of bands of hyperspectral sensors might provide an ability to handle the more complex near-shore environment. Potential static products to consider include target detection and near-shore bottom characterization in support of mine countermeasures and battlespace mapping and possibly submarine operations. To this we can also add near-shore bathymetry in support of route survey, battlespace mapping, anti-submarine warfare and submarine operations. While not requiring hyperspectral sensing, HSI could keep playing a role (e.g. selection of optimal bands) in the development of new dynamic products for both near-shore and offshore applications. Overall, this assessment point to possible follow-up for marine

In this chapter, we discussed a wide variety of military applications resulting from the exploitation of reflective hyperspectral imagery. These applications were demonstrated in the DRDC HYMEX project, allowing DRDC and the Canadian Forces stakeholders to get more familiar with the military utility of hyperspectral imagery. While some of these applications such as target detection are relatively mature and are near to operational deployment, others still require further development but are representative of the unique capability of hyperspectral remote sensing. The many datasets that were acquired and the algorithms and exploitation tools that were developed in the project are being used to continue the development of hyperspectral technology at DRDC. One avenue that is being pursued is the development of an airborne hyperspectral real-time target detection demonstration system. We are also looking at opportunities to further develop the land mapping and marine applications areas as well as potential space-based demonstration with

Antunes, M.S.; Ha, S.B.; Tewari-Singh, N.; Morey, K.J.; Trofka, A.M.; Kugrens, P.; Deyholos,

Ardouin, J.-P.; Lévesque, J. & Rea, T.A. (2007) A Demonstration of Hyperspectral Image

Beeftink, H.H. (1951) Some observations on tamarack or eastern larch. *Forestry Chronicle*,

Bernstein, L.S.; Adler-Goldstein, S.M.; Sundberg, R.L., Levine, R.Y.; Perkins, T.C.; Berk, A.;

M. & Medford J.I. (2006) A synthetic de-greening gene circuit provides a reporting system that is remotely detectable and has a re-set capacity, *Plant Biotechnology J.*,

Exploitation for Military Applications, *Proc. of the 10th International Conference on Information Fusion (FUSION 2007)*, Quebec, Canada, (9-12 July 2007), pp. 1-8., ISBN

Ratkowski, A.J.; Felde, G. & Hoke, M.L. (2005) A new method for atmospheric correction and aerosol optical property retrieval for VIS-SWIR multi- and hyperspectral imaging sensors : QUAC (QUick Atmospheric Correction), *IEEE International Geoscience and Remote Sensing Symposium*, (25-27 July 2005), pp. 3549-

applications development with hyperspectral sensors.

Vol. 4, No. 6, (November 2006), pp. 605-622

**6. Conclusion** 

international partners.

978-0-662-45804-3

Vol. 27, No. 1, (1951), pp. 38-39

3552, ISBN 0-7803-9050-4

**7. References** 


Rogge, D.; Rivard, B.; Deyholos, M.; Lévesque, J. & Faust, A. (2008) Toxic Industrial

Roy, V. (2010) Hybrid algorithm for hyperspectral target detection, *Proc of SPIE Algorithms* 

Sentlinger, G.; Davenport M., & Ardouin, J.-P. (2003) Automated Target Recognition in

Settle, J. (2002) On constrained energy minimization and the partial unmixing of

Settle, J. (2004) On the Use of Remotely Sensed Data to Estimate Spatially Averaged

Smith, G. M. & Milton, E.J. (1999) The use of the empirical line method to calibrate remotely

Van Chestein, Y. (2011) *Comparative evaluation of the Mercury classification algorithm: On the* 

Webster, A.H.; Davenport, M.R. & Ardouin, J.-P. (2006) 3D Deconvolution of Vibration

Williams, D.; Vachon, P.W; Wolfe, J.; Robson, M.; Renaud, W.; Perrie, W.; Osler, J.; Isenor,

*Automatic Target Recognition XIII*, Vol 5094, (April 2003)

(March 2002), pp. 718-721, ISSN 0196-2892

(March 2004), pp. 620-631, ISSN 0196-2892

*Research 2006*, Bar Harbor, MA., (May 2006).

Report, DRDC Ottawa ECR 2009-139, (September 2009)

MA, (July 2008)

2653-2662

2011)

7695, May 2010, pp. 1-10

Chemical Effects on Poplar, Canola, and Wheat detectable over the 450-2500 nm Spectral Range, *Intl. Geoscience & Remote Sensing Symposium (IGARSS 2008)*, Boston,

*and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI*, Vol.

Hyperspectral Imagery using Subpixel Spatial Information, *Proc. SPIE Aerosense –* 

multispectral images, *IEEE Trans. on Geoscience and Remote Sensing*, Vol. 40, No. 3,

Geophysical Variables, *IEEE Trans. on Geoscience and Remote Sensing*, Vol. 42, No. 3,

sensed data to reflectance, *International Journal of Remote Sensing,* Vol. 20, (1999), pp.

*influence of the number of bands on classification accuracy using hyperspectral data*, Defence R&D Canada - Valcartier Technical Memorandun, TM 2010-385, (March

Corrupted Hyperspectral Images, *International Symposium on Spectral Sensing* 

A.W.; Larouche, P.; Jones, C. (2009) *Spaceborne Ocean Intelligence Network – SOIN – fiscal year 08/09 year-end summary*, Defence R&D Canada - Ottawa External Client
