rapid prototyping of hyperspectral image analysis algorithms for ...€¦ · nasa rpc experiment...

Rapid Prototyping of Hyperspectral Image Analysis Algorithms for Improved Invasive Species Decision

Support Tools

FINAL REPORT NASA GRANT # DONNS06AA65D USMMRCSSC1216200565D 06040327

SUBMITTED TO: TED MASON, NASA STENNIS SPACE CENTER

SUBMITTED BY: LORI MANN BRUCE, PH.D. GEORESOURCES INSTITUTE

MISSISSIPPI STATE UNIVERSITY BOX 9627

MISSISSIPPI STATE, MS 39762-9627 PHONE – 662-325-8430

EMAIL – [email protected]

NASA RPC Experiment Final Report, Lori Mann Bruce, Ph.D., Mississippi State University, page 2/15

Introduction Nonnative invasive species adversely impact ecosystems, causing loss of native plant

diversity, species extinction, and impairment of wildlife habitats. As a result, over the past

decade federal and state agencies and nongovernmental organizations have begun to work

more closely together to address the management of invasive species. In the 2005 fiscal year,

approximately $500M was budgeted by U.S. Federal Agencies for the management of

invasive species, where the primary funding included the following: DOD-$15M, USDA-

$470M, and DOI-$11M [Simberloff 2005]. Despite extensive expenditures, most of the

methods used to detect and quantify the distribution of these invaders are ad hoc, at best.

Likewise, decisions on the type of management techniques to be used or evaluation of the

success of these methods are typically non-systematic. More efficient methods to detect or

predict the occurrence of these species, as well as the incorporation of this knowledge into

decision support systems, are greatly needed.

The invasive species known as salt cedar or tamarisk (Tamarix ramosissima) is a

particular problem in the U.S.’s desert southwest, where it is displacing the native

cottonwood, willow, and other native plants. Tamarisk shrubs, or trees, are extremely

competitive against native vegetation because they aggressively consume the water supply.

Since tamarisk can re-grow from root crown buds, even after burning, the current

management practices for tamarisk involve combinations of chemical, mechanical, and

biological techniques. Thus, detection of tamarisk when it is in it earliest growing stages,

through the use of remote sensing, could greatly reduce the cost associated with this invasive

species.

In this rapid prototyping capabilities (RPC) experiment, recently developed analysis

techniques for hyperspectral imagery were prototyped for inclusion in the National Invasive

Species Forecasting System (NISFS). The new decision methodologies were tested using

hyperspectral data obtained with a handheld spectroradiometer and hyperspectral imagery

obtained via NASA’s HYPERION sensor. The results of this RPC experiment clearly

demonstrated the viability of applying remotely sensed imagery, particularly hyperspectral

such as that of HYPERION, to the problem of invasive species detection, as well as the image

analysis methods’ potential for improving the existing NISFS decision support system.


Figure 1: Tamarisk stand in Colorado. Native plants are unable to penetrate the thick stands of tamarisk [Photograph: Tim Carlson].

Figure 2: Removal of dead tamarisk using fire at the Bosque del Apache NWR, NM [“Cost Components for Non-Native Phreatophyte Control, November 2006”, Tamarisk Coalition]


Existing NISFS Decision Support System

In the three years prior to this RPC experiment, the PIs participated in the NASA invasive

species science team meetings for the development of the NISFS

[http://bp.gsfc.nasa.gov/isfs_people.html]. We presented our work at the science team

meetings, and these presentations are listed on the NISFS website

[http://bp.gsfc.nasa.gov/isfs_news.html]. As a result of our meetings with the science team,

we worked with members from the team to develop hyperspectral image analysis tools for

automated detection of invasive vegetation. Likewise, the PIs are beginning work on a task

force, as part of the science team, to develop a “library” of spectral-temporal profiles for

invasives. It is currently planned for the National Institute of Invasive Species Science

(NIISS) to serve as a clearinghouse, and the library will be developed in conjunction with the

USGS spectral library program. This is particularly of interest to the RPC experiment, since

the “library” could be potentially used as training data for future (non-tamarisk) applications

of the experiment’s resulting tools: hyperspectral image analysis algorithms for detecting

invasive species.

RPC Experiment: Methodologies

Hyperspectral Imagery Analysis and Automated Target Recognition Techniques

Over the past six years, the PIs have researched the use of remotely sensed multispectral

and hyperspectral reflectance data for the detection of invasive vegetative species [refs 1-15].

As a result, the PI has designed, implemented, and benchmarked various target detection

systems that utilize remotely sensed data. These systems have been designed to make

decisions based on a variety of remotely sensed data, including high spectral/spatial resolution

hyperspectral signatures (1000’s of spectral bands with spectral resolutions of approximately

3nm, such as those measured using ASD™ handheld devices), moderate spectral/spatial

resolution hyperspectral images (100’s of spectral bands, such as HYPERION imagery), and

low spectral/spatial resolution images (such as LANDSAT or MODIS imagery). These


algorithms (discrete analysis methods) include hyperspectral exploitation techniques such as

the following dimensionality reduction methods:

• Best spectral band selection (i.e. stepwise-LDA band selection)

• Optimized spectral band grouping

• Stepwise PCA component selection

The PIs have extensive experience with combining these recently-developed methods with

conventional feature reduction methods, such as Fisher’s LDA, and conventional classifiers,

such as nearest neighbor and maximum likelihood classifiers. The end result is an end-to-end

automated target recognition (ATR) system for detecting invasive species. The outputs of

these systems can be invasive prediction maps, as well as quantitative accuracy assessments

like confusion matrices, user accuracies, and producer accuracies.

Figure 3. Simplified block diagram of discrete analysis pattern recognition techniques applied to hyperspectral imagery.

Hyperspectral Data and Verification and Validation Techniques

Collaborators at Colorado State University (CSU) and USGS supplied the necessary

ground truth via field surveys. Collaborators at NASA supplied hyperspectral signatures from

handheld sensors, as well as ASTER co-registered HYPERION imagery of the field sites.

The ground truth and hyperspectral data were both collected in the Grand Staircase-Escalante

National Monument, in southern Utah. All model design, algorithm development,

experimental analysis, and verification and validation (V&V) were conducted by the MSU

researchers.

Dimensionality Reduction

Feature Optimization Classification

Pixel Label (Map)


Figure 4. Hackberry Canyon in the Grand Staircase-Escalante National Monument was used as the field study test sight for all experimental data collection (ground truth and HYPERION imagery).


Figure 5. Airborne and satellite imagery of field study test site, Hackberry Canyon in the Grand Staircase-Escalante National Monument.


July 2004

November 2004 December 2004

October 2004August 2004July 2004

November 2004 December 2004

October 2004August 2004

Figure 6. Temporal photos of tamarisk in Hackberry Canyon, Grand Staircase-Escalante National Monument. [Courtesy of Paul Evangelista].

Figure 7: HYPERION imagery color composite of Hackberry Canyon in the Grand Staircase-Escalante National Monument, with ground truth points overlaid. Red dots indicate ground truth points of tamarisk presence and blue dots indicate ground truth points of non-tamarisk.


RPC Experiment: Motivation

For all of the research endeavors described above, the PIs have developed numerous

advanced signal and image processing methodologies, as well a suite of associated software

modules. These methodologies have been extensively researched, benchmarked, and

published in refereed conferences and journals. However, prior to this RPC experiment, the

use of the prototype software modules has been primarily contained to in-house use at

Mississippi State University (MSU). This RPC experiment enabled

(i) a systematic verification and validation of the hyperspectral image analysis

algorithm using both handheld spectroradiometer data and satellite image data,

namely HYPERION imager;

(ii) a formal collaboration between remote sensing and invasive species researchers at

MSU (Lori Mann Bruce, et al), USGS (Tom Stohlgren, et al), CSU (Paul

Evangelista, et al), and NASA (Jeff Morisette, et al);

(iii) a direct comparison of invasive species mapping capabilities of the existing

decision support system (NISFS) and the proposed methods (automated analysis

of HYPERION imagery).

Experimental Results

The results of this research project clearly demonstrate the viability of applying

remotely sensing imagery, particularly hyperspectral such as that of HYPERION, to the

problem of invasive species detection. As expected, the results show that the tamarisk

detection accuracy depends on both spatial and spectral resolutions. The newly developed

image processing and pattern recognition methods resulted in accuracies as high as 90% for

discriminating tamarisk from native vegetation, such as cottonwoods and willows, when the

input hyperspectral sensors had a spectral resolution in the range of 30-100nm and a spatial

resolution that allowed for target abundances to be greater than 50%, i.e. mixed pixels had at

least 50% ground coverage of tamarisk. These results are very promising and clearly indicate

the power and practicality of hyperspectral remote sensing for invasive species detection.


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Figure 8. High spectral and spatial resolution hyperspectral signatures of the target invasive vegetation (tamarisk) and nontarget non-invasive vegetation (cottonwood and willow). Signatures are preprocessed with waterband interpolation, truncation to 1650 bands, and normalization to [0,1].


Figure 9: Classification accuracies (overall accuracies of two class system: tamarisk

vs. non-tamarisk (willow, cottonwood, etc)) for varying spatial and spectral resolutions.


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Figure 10. HYPERION hyperspectral signatures of the target invasive vegetation (tamarisk - red) and nontarget non-invasive vegetation (cottonwood and willow - blue). Signatures raw data prior to any preprocessing (removal of water bands, normalization, etc).


Target Abundance

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Figure 11. Classification accuracies (overall accuracies of two class system: tamarisk vs. non-tamarisk (willow, cottonwood, etc)) for HYPERION mixed pixels for varying target abundance.

Transition of Results to ISS

The USGS-NASA team was also awarded a “Earth Science REASoN – CAN” grant to

develop an Invasive Species Data Service (ISDS) to provide customized, easily accessible

data products and tools to support invasive species management and policy decision-making.

The PIs have begun discussions with the principal investigators of the ISDS project on how to

use their ISDS to integrate our newly developed tools into the NISFS, as part of a future

Integrated Systems Solutions (ISS) project.

Acknowledgments The researchers gratefully acknowledge our Colorado State University and USGS

collaborators, including Tom Stohlgren and Paul Evangelista, and our NASA collaborators,

including Jeff Morrisette and Steve Ma.


References [1] J.E. Ball, L.M. Bruce, and N.H. Younan, “Hyperspectral pixel unmixing via spectral

band selection and DC insensitive singular value decomposition,” IEEE Geoscience and Remote Sensing Letters, Vol. 4, No. 3, Jul. 2007.

[2] L.M. Bruce, A. Mathur, J.D. Byrd, “Denoising and Wavelet-Based Feature Extraction of MODIS Multi-Temporal Vegetation Signatures,” GIScience & Remote Sensing, vol. 43, pp. 170-180, 2006.

[3] J. Li and L.M. Bruce, “Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals,” IEEE Trans. Geoscience and Remote Sensing, vol. 42, no. 3, pp. 644-649, March 2004. (See Correction to “Wavelet-Based Feature Extraction for Improved Endmember Abundance Estimation in Linear Unmixing of Hyperspectral Signals”, IEEE Trans. Geoscience and Remote Sensing, vol. 42, no. 5, pp. 1122, May 2004.)

[4] S. Prasad, L.M. Bruce, “Limitations of Subspace LDA in Hyperspectral Target Recognition Applications,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Barcelona, Spain, July 2007.

[5] S. Prasad, L.M. Bruce, “Hyperspectral Feature Space Partitioning via Mutual Information for Data Fusion,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Barcelona, Spain, July 2007.

[6] T.R. West, S. Prasad, L.M. Bruce, “Multiclassifiers and Detection Fusion in the Wavelet Domain for Exploitation of Hyperspectral Data,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Barcelona, Spain, July 2007.

[7] J. Ball, T.R. West, S. Prasad, L.M. Bruce, “Level Set Hyperspectral Image Segmentation using Spectral Information Divergence (SID) based Best Band Selection,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Barcelona, Spain, July 2007.

[8] S. Prasad, L.M. Bruce, "Information Theoretic Partitioning and Confidence based Weight Assignment for Multi-Classifier Decision Level Fusion in Hyperspectral Target Recognition Applications," Proc. of the SPIE Defense and Security Symposium, Orlando, Florida, USA, April 2007.

[9] Mathur, L.M. Bruce, D.W. Johnson, W. Robles, J. Madsen, “Exploiting Hyperspectral Hypertemporal Imagery with Feature Clustering for Invasive Species Detection,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Denver, CO, August 2006.

[10] J. Ball, L.M. Bruce, “Level Set Hyperspectral Segmentation: Near-Optimal Speed Functions using Best Band Analysis and Scaled Spectral Angle Mapper,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Denver, CO, August 2006.

[11] Mathur, L.M. Bruce, D.W. Johnson, W. Robles, J. Madsen, “Automated Stepwise Selection of Hyperspectral Hypertemporal Features for Target Detection,” Proc. IEEE Geoscience and Remote Sensing Symposium(IGARSS), Denver, CO, August 2006.


[12] T.R. West, L.M. Bruce, “Detecting Invasive Species via Hyperspectral Imagery using Sequential Projection Pursuits,” Proc. IEEE Geoscience and Remote Sensing Symposium (IGARSS), Denver, CO, August 2006.

[13] Mathur, L.M. Bruce, “Identification of Pertinent Regions in Spectro-Temporal Maps for Vegetative Target Detection,” Proceedings of the American Society of Photogrammetry and Remote Sensing 2006 Annual Conference (ASPRS 2006), Reno, NV, May 2006.

[14] J. Ball, L.M. Bruce, “Accuracy Analysis of Hyperspectral Imagery Classification using Level Sets,” Proceedings of the American Society of Photogrammetry and Remote Sensing 2006 Annual Conference (ASPRS 2006), Reno, NV, May 2006.

[15] D.W. Johnson, L.M. Bruce, “Spatial and Spectral Resolution Effects on the Use of Remotely Sensed Data for Detection of Invasive Species,” Proc. IEEE Geoscience and Remote Sensing Symposium (IGARSS), Seoul, Korea, July 25-29, 2005.

Appendix A: Budget I. Salaries and Wages 64,070$

1a. Faculty Salaries - Lori Mann Bruce$108,542 annual (5% academic year) 4,070$

1b Research Associate - John Ball$60,000 annual @ 100% $60,000

II. Fringe Benefits 23,362$ 2a. 1a @ 29.72 1,210$ 2b 1b @ 29.72 17,832$ 2c Tuition @ $480.00/mo per student 4,320$

III. Equipment -$

IV. Supplies (Expendable Supplies) -$

IV. Travel 8,500$ 4a. In-state 1,000$ 4b. Out-of-State 7,500$

V. ADP Expenses -$ TOTAL DIRECT COSTS 95,932$

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