1. Pining for Data II: The Empirical Results Strike Back
Zach Schira
Analytics Hub

2. Goals
Use NEON hyperspectral and Lidar data to accurately classify plant species using machine learning
Example Hyperspectral Cube

3. Using Neural Networks and SVM’s
Results:
55-75% Accuracy
Drawbacks:
Don’t account for order/spatial information well
Can only detect one object at a time

4. Using Neural Networks and SVM’s
Randomized

5. Using a Convolutional Neural Network
Advantages:
Much better at accounting for order/spatial information
Can perform multiple classifications at once
Can locate objects within images
Disadvantages:
Often more computationally expensive
Difficult to implement

6. Basics of Convolutional Neural Networks
Example of a single convolutional layer output

7. Data Processing Data comes from multiple sources of varying quality
Machine learning does not respond well to missing data
426 hyperspectral bands + 1 Lidar band = lots of data
One hyperspectral band

8. Using Principal Component Analysis (PCA)
PCA can reduce dimensions of data to save memory and computing time
Useful when data is highly correlated
Reflectance of random points in 2 wavelengths

9. PCA Principals
Uses linear combinations variables to transform data to a set of uncorrelated variables with dimension less than or equal to the original data
“Summarizes” variance in fewer dimensions
PCA example

10. Results Data reduced down to 3 bands
2 hyperspectral
1 Lidar
40 x 40 plots created with labeled species

11. Going forward with Faster R-CNN
R-CNN uses a series of convolutional and regular neural network layers to predict a classification for each window location

12. Questions?

13. References
Ren, Shaoqing, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
Networks." IEEE Transactions on Pattern Analysis and Machine Intelligence (2016): 1. Microsoft Research. Web. 8 Mar
Wasser, Leah. "Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R." NEON Data Skills. NEON, n.d. Web. 08
Mar
Dumoulin, Vincent. "A Guide to Convolution Arithmetic for Deep Learning." (n.d.): n. pag. 24 Mar Web. 8 Mar