# Presentation: 499471

## Cross-Cutting Data Analysis Techniques

*Dr. Lori Mann Bruce*
*Electrical & Computer Engineering*
*GeoResources Institute*
*Mississippi State University*
*bruce@ece.msstate.edu*

## Current Research Areas

- DSP and ATR  for
- 	Hyperspectral, Multispectral, Lidar

## Current Research Areas

- Spatial and Spectral Feature Extraction
- Temporal Change Detection
- Hyperspectral Dimensionality Reduction
- Multiclassifiers and Decision Fusion
- Pixel Unmixing
- Effects of Archiving on Data Analysis   (Compression and Watermarking)

## Spatial and Spectral Feature Extraction

-  Feature Extraction for
-  Supervised Classification
-  Wavelet transforms, Karhunen-Loeve transforms
-  Singular value decomposition
-  Maximum-likelihood, nearest-neighbor classifiers
-   Unsupervised Classification
-   Self-organizing maps
-  Fully automated target detection systems

## Weed Detection 

- Weed Detection
- (Spatial Features in Multispectal Images)

## NDVI Texture Image

- False Color Image

- Threshold to Plant Density

## Hyperspectral Dimensionality Reduction

-  Best Spectral Band/Range Selection
-  Multivariate Statistical Approaches
-  Signal Transformation
-   Discrete Wavelet Transforms
-   Karhunen-Loeve Transforms
-   Projection Pursuits

## Hyperspectral Dimensionality Reduction

- (Histogram of optimum
- set of bands shown in blue)

- PCA

- LDA

## Spectral Dimensionality (BU-GLDB Algorithm for finding best bases)

- Spectral Dimensionality (BU-GLDB Algorithm for finding best bases)

- C1D1

- C3D3

- Find the best pair of spectral bands that can be combined. The criteria used is the product of the correlation between pair of bands (C) and discrimination capability (D) of the combined band pair.

- C2D2

- C4D4

- C6D6

- C5D5

- C7D7

- C9D9

- C8D8

- C1D1

- C3D3

- C2D2

- C4D4

- C5D5

- C6D6

- C8D8

- C7D7

- C1D1

- C3D3

- C2D2

- C4D4

- C5D5

- C6D6

- C7D7

- C3D3

- C2D2

- C4D4

- C5D5

- C6D6

- C7D7

- Merging of these two groups result in a poorer CD metric than the original groups.

## Multiclassifiers & Decision Fusion

- Feature
- Extractor

- Classifier

- Feature
- Extractor

- Classifier

- Feature
- Extractor

- Classifier

- Feature
- Extractor

- Classifier

- Feature
- Extractor

- Classifier

- Decision Fusion

## Accurate Pixel Unmixing

***Mixed Pixel***

***Endmembers & Abundances***

***Spectral Unmixing***

- 3 Endmembers:
- 	2 vegetations
- 	1 soil

## Recent Refereed Journal Publications

-  C.H. Koger, L.M. Bruce, D.R. Shaw, K.N. Reddy, “Wavelet Analysis of Hyperspectral        Reflectance Data for Detecting Pitted Morningglory (*Ipomoea lacunosa*) in Soybean (*Glycine max*), *Remote Sensing of Environment*, vol. 86, no. 1, pp. 108-119, June 2003.
- L.M. Bruce, C.H. Koger, J. Li, "Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction," *IEEE Trans. Geoscience. Remote Sensing*, vol. 40, no. 10, pp. 2331-2338, Oct 2002.
-  L.M. Bruce, J. Li and Y. Huang, "Automated detection of subpixel hyperspectral targets with adaptive multichannel discrete wavelet transform," *IEEE Trans. Geoscience Remote Sensing*, vol. 40, no. 4, pp.977-980, April 2002.
-  L.M. Bruce and J. Li, "Wavelets for computationally efficient hyperspectral derivative analysis," *IEEE Trans. Geoscience Remote Sensing*, vol.39, no.7, pp.1540-1546, July 2001.
-  L.M. Bruce, C. Morgan, S. Larsen, "Automated detection of subpixel targets with continuous and discrete wavelet transforms," *IEEE Trans. Geoscience and Remote Sensing*, vol. 39, no. 10, pp. 2217-2226, Oct 2001.
-  J.-G Cao, J.E. Fowler, N.H. Younan, “An image-adaptive watermark based on a redundant wavelet transform,” *Proc Image Processing*, vol. 2, no. 10, pp. 277 -280, 2001.
-  J.E. Fowler, H. Li, “Wavelet transforms for vector fields using omnidirectionally balanced multiwavelets,” *IEEE Trans Signal Processing*, vol. 50, no. 12, pp. 3018-3027, Dec 2002.
-  J.E. Fowler, D.N. Fox, “Embedded wavelet-based coding of three-dimensional oceanographic images with land masses,” *IEEE Trans. Geoscience and Remote Sensing*, vol. 39, no. 2, pp. 284-290, Feb 2001.

## ?

- Invasive Species Detection and Assessment

## Spectral Baselines

## Spectral Baselines

**Kudzu**

**Soda apple**

**Horseweed**

**Sicklepod**

**Dogfennel**

## Haar

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- _Haar_

## 5 Class Results

- (Classification Accuracies and Confidence Intervals)

## Invasive Species Detection: Automated Algorithm Development

-  Airborne level research
-  Collect hyperspectral/multispectral images
-  Collect ground truth
-  Combine spectral features with spatial features
-  Classify vegetation as target or non-target

## Invasive Species Detection: Automated Algorithm Development

- Spectral
- Features

- Spatial
- Features

- Invasive Species Detection: Automated Algorithm Development

## Collection Date: August 30, 2000

- Collection Date: February 1, 2001

- Collection Date: July 16, 2001

## (This image is used as the background for the presentation.)

**(This image is used as the background for the presentation.)**
**Location Noxious Weed Studies -- Oswalt**
**Date June 5, 2001**
**Sensor ITD (Spectral Visions) RDACS**
**Altitude 6000 ft.**

## Cogongrass (Imperata Cylindrica)

**	****Invasive species introduced into southern USA in early 1900’s**
** ****	****The seventh worst weed in the world**
** ****	****Listed in the United States Federal Noxious Weeds List **

##  Example Case Study

** ****Example Case Study**

## Thank You