**1. Introduction**

492 Remote Sensing – Applications

Zhou, Q., Liu, J., Host-Madsen, A., Boric-Lubecke, O., Lubecke, V. (2006) Detection of

pp.2100-2107.

pp.1160-1163.

of a subject in bed via a pneumatic method.IEEE Trans Biomed Eng, 52(12),

multiple heartbeats using Doppler radar," IEEE ICASSP 2006 Proceedings, 2,

Airborne hyperspectral imagers have been available from various providers for many years and their performance keeps improving. On the other hand, space-based hyperspectral sensors have only been available from few exploratory missions such as NASA Hyperion on EO-1 (Pearlman et al, 2003) and ESA CHRIS on Proba (Cutter et al, 2003). In recent years, there have been many civilian space missions being planned in different countries (Buckingham & Staenz, 2008), as well as military space demonstrations (Cooley et al, 2006).

Given the increase in potential space-based hyperspectral sensors, Defence R&D Canada (DRDC), which is part of the Canadian department of National defence, began in 2005 a project to demonstrate the military utility of space-based reflective hyperspectral imagery (0.4-2.5 microns) to the Canadian Forces (CF). The project is called HYperspectral iMage EXploitation (HYMEX) and ended its activities in 2010 (Ardouin et al, 2007).

Before the HYMEX project, DRDC had been conducting and sponsoring R&D in the area of hyperspectral image exploitation for a number of years to explore its various possibilities (Davenport & Ressl, 1999; Sentlinger et al, 2003; Webster et al, 2006). The focus of this work was on military target detection applications. In parallel with these activities, the Canadian remote sensing community has also been active in developing hyperspectral applications for various civilian applications related to forestry, agriculture, fisheries, mineral exploration and environmental monitoring (Buckingham et al, 2002). Many hyperspectral techniques developed for civilian applications can be applied to military applications such as terrain characterization.

Building on previous efforts at DRDC and with support from Canadian industry, academic institutions and other government departments, the HYMEX project identified a set of applications and related algorithms to be demonstrated to the Canadian Forces.

This chapter presents an overview of the project, beginning with a description of its main activities (Section 2.0), including field trials, data analysis and algorithms evaluation and the development of an image exploitation software. Then, for each application areas, target detection (Section 3.0), land mapping (Section 4.0) and marine mapping (Section 5.0), we

Demonstration of Hyperspectral Image Exploitation for Military Applications 495

limited way of a) how the performance varies in different environments, b) the advantage of hyperspectral (HSI) over multispectral (MSI) imagery and c) the effect of varying ground sampling distance (GSD) via the analysis of airborne data at different altitudes or of

Fig. 1. HOST two main windows: the visualization window on the left and the control

*HOST demonstrations*: We developed the HOST software in three successive iterations. Each iteration ended with a live or hands-on demonstration of the tools to CF stakeholders and image analysts. Feedback from the demonstration participants were integrated in the following iterations and the selection of the algorithms to be integrated for the iteration was based on interim data analysis results. As explained above, HOST is an add-on to ENVI. While ENVI is a powerful exploitation package for advanced hyperspectral imagery users, HOST is oriented towards military end-users with introductory knowledge of hyperspectral image exploitation. In order to present a simplified and more uniform user interface than ENVI, HOST is organized in two main windows as illustrated in Figure 1: the visualization window and the control window. The HOST visualization window regroups, in a single window, many of the familiar visualization tools offered by ENVI (image display, plot display, available bands list, region of interest tool and the vector layer manipulation tool). By regrouping these tools in a single window, the user can more easily keep track of these functions as they are applied to specific hyperspectral images. The control window provides, in a single window, an interface to the parameters of the different advanced exploitation algorithms offered by HOST. The user would typically use the control window to setup batch scripts to process many hyperspectral images without user intervention. The HOST control window is organized in different logical categories of algorithms such as: pre-

different sensors.

window on the right

discuss some of the most promising algorithms and show examples of application of these algorithms.

#### **2. HYMEX applications and activities**

The HYMEX project addressed applications divided in 3 categories:


As mentioned in the introduction, DRDC developed expertise and advanced exploitation techniques for target detection over the years but had limited expertise for terrain analysis and water mapping. To identify available techniques for the last 2 categories, HYMEX conducted a survey of about 60 different groups from industry, academia and other government departments forming the Canadian remote sensing community. Most algorithms from the groups that responded have been included in the project.

The project delivered a suite of near-operational hyperspectral image exploitation tools called HOST (Hyperspectral Operational Support Tools). HOST is developed in the IDL language as an add-on to a commercial-off-the-shelf package called ENVI available from ITT Visual Information Solutions. ENVI is already a widely used platform for hyperspectral image analysis. HOST builds onto ENVI to provide tools specifically oriented for the needs of the Canadian Forces. The HYMEX System Integrator responsible for the development of HOST was MacDonald Dettwiler and Associates (Richmond, BC).

HYMEX adopted an algorithms validation strategy that led to the selection of the best algorithms to be integrated into HOST and explore the performance limits of these algorithms. Knowing these limits is an important element of information for transitioning the tools into operational use by the Canadian Forces. The validation strategy included the following components:

*Field Trials:* The HYMEX project conducted several field trials at different locations across Canada to acquire remotely sensed hyperspectral imagery and ground truth for testing the various algorithms. Each location had specific characteristics likely to affect the performance of the algorithms. Marine trials were conducted in two different locations (East and West coast). Land trials were conducted in an Acadian forest (CFB Gagetown, NB), a boreal forest (CFB Valcartier, QC), an aspen dominant forest (CFB Wainwright, AB) and in prairie grassland (CFB Suffield, AB). One trial was conducted in winter under snow conditions. While some space-based hyperspectral imagery was acquired, the project main data type came from airborne hyperspectral sensors. Airborne data contains similar information as space-based sensors, and can be used in the interim to evaluate algorithms. In fact, even though HYMEX focused on demonstrating the utility of space-based imagery, the results can easily be transposed into the utility of airborne sensors.

*Data analysis:* All HYMEX algorithm providers analyzed trial data using performance metrics approved by DRDC. In addition to providing quantitative performance evaluation and comparisons of the algorithms, the overall data analysis allowed the demonstration in a

discuss some of the most promising algorithms and show examples of application of these

1. Target detection and identification: This includes targets such as military vehicles,

As mentioned in the introduction, DRDC developed expertise and advanced exploitation techniques for target detection over the years but had limited expertise for terrain analysis and water mapping. To identify available techniques for the last 2 categories, HYMEX conducted a survey of about 60 different groups from industry, academia and other government departments forming the Canadian remote sensing community. Most

The project delivered a suite of near-operational hyperspectral image exploitation tools called HOST (Hyperspectral Operational Support Tools). HOST is developed in the IDL language as an add-on to a commercial-off-the-shelf package called ENVI available from ITT Visual Information Solutions. ENVI is already a widely used platform for hyperspectral image analysis. HOST builds onto ENVI to provide tools specifically oriented for the needs of the Canadian Forces. The HYMEX System Integrator responsible for the development of

HYMEX adopted an algorithms validation strategy that led to the selection of the best algorithms to be integrated into HOST and explore the performance limits of these algorithms. Knowing these limits is an important element of information for transitioning the tools into operational use by the Canadian Forces. The validation strategy included the

*Field Trials:* The HYMEX project conducted several field trials at different locations across Canada to acquire remotely sensed hyperspectral imagery and ground truth for testing the various algorithms. Each location had specific characteristics likely to affect the performance of the algorithms. Marine trials were conducted in two different locations (East and West coast). Land trials were conducted in an Acadian forest (CFB Gagetown, NB), a boreal forest (CFB Valcartier, QC), an aspen dominant forest (CFB Wainwright, AB) and in prairie grassland (CFB Suffield, AB). One trial was conducted in winter under snow conditions. While some space-based hyperspectral imagery was acquired, the project main data type came from airborne hyperspectral sensors. Airborne data contains similar information as space-based sensors, and can be used in the interim to evaluate algorithms. In fact, even though HYMEX focused on demonstrating the utility of space-based imagery, the results

*Data analysis:* All HYMEX algorithm providers analyzed trial data using performance metrics approved by DRDC. In addition to providing quantitative performance evaluation and comparisons of the algorithms, the overall data analysis allowed the demonstration in a

algorithms from the groups that responded have been included in the project.

HOST was MacDonald Dettwiler and Associates (Richmond, BC).

can easily be transposed into the utility of airborne sensors.

2. Land mapping applications: This includes the characterization of soil and vegetation. 3. Marine mapping applications: This includes beach characterization and near-shore

algorithms.

following components:

**2. HYMEX applications and activities** 

The HYMEX project addressed applications divided in 3 categories:

camouflages and various man-made materials.

bathymetry, as well as water color mapping.

limited way of a) how the performance varies in different environments, b) the advantage of hyperspectral (HSI) over multispectral (MSI) imagery and c) the effect of varying ground sampling distance (GSD) via the analysis of airborne data at different altitudes or of different sensors.

Fig. 1. HOST two main windows: the visualization window on the left and the control window on the right

*HOST demonstrations*: We developed the HOST software in three successive iterations. Each iteration ended with a live or hands-on demonstration of the tools to CF stakeholders and image analysts. Feedback from the demonstration participants were integrated in the following iterations and the selection of the algorithms to be integrated for the iteration was based on interim data analysis results. As explained above, HOST is an add-on to ENVI. While ENVI is a powerful exploitation package for advanced hyperspectral imagery users, HOST is oriented towards military end-users with introductory knowledge of hyperspectral image exploitation. In order to present a simplified and more uniform user interface than ENVI, HOST is organized in two main windows as illustrated in Figure 1: the visualization window and the control window. The HOST visualization window regroups, in a single window, many of the familiar visualization tools offered by ENVI (image display, plot display, available bands list, region of interest tool and the vector layer manipulation tool). By regrouping these tools in a single window, the user can more easily keep track of these functions as they are applied to specific hyperspectral images. The control window provides, in a single window, an interface to the parameters of the different advanced exploitation algorithms offered by HOST. The user would typically use the control window to setup batch scripts to process many hyperspectral images without user intervention. The HOST control window is organized in different logical categories of algorithms such as: pre-

Demonstration of Hyperspectral Image Exploitation for Military Applications 497

Fig. 3. Left) Example of controlled abundance target. Right) Spectral signatures of the beige paint at different albedo levels, as measured in field conditions using an ASD FieldSpec Pro

Fig. 4. Left) SWIR 3-colors composite of targets, imaged at a 2.4m GSD (coarsest resolution). Right) Background (red) and target (blue) signatures as measured by the airborne sensor,

DRDC favours automated and adaptive approaches to hyperspectral target detection, minimizing user interaction and processing time as much as possible. In this context, we

1. We first manually identified and removed 12 out of the original 100 spectral bands due to their low signal to noise level. We also removed unusable part of the imagery due to

2. We then converted the imagery from at-aperture radiance to apparent reflectance units using the empirical line method (Smith & Milton, 1999), with five very large targets (greater than 10x10 pixels) of known reflectance. This was possible due to the controlled environment of this imagery collection; else we would typically have used a semiempirical technique such as the QUick Atmospheric Correction (QUAC) method

3. The CEM algorithm requires a description of the background 2nd order statistics (mean and covariance). For this calculation, we identified a subset of background pixels from the complete image using the RX anomaly detection algorithm (Reed & Yu, 1990) by keeping only the first 90% lowest scoring pixels. Second order statistics were then

sensor vignetting. This is typically done only once for a given sensor.

compared to the target library signature (black).

evaluated using this limited set of pixels.

(Bernstein et al, 2005).

used the following processing chain for this dataset exploitation:

spectrometer.

processing, atmospheric correction, exploitation and interactive tools. A Navigation Tool also allows loading customized task descriptions with links to the HOST user interface. This guides the user through the algorithms needed to accomplish a task and the selection of the parameters for those algorithms.
