Pining for Data II: The Empirical Results Strike Back

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Presentation transcript:

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

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

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

Using Neural Networks and SVM’s Randomized

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

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

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

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

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

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

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

Questions?

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. 2017. Wasser, Leah. "Intro to Working with Hyperspectral Remote Sensing Data in HDF5 Format in R." NEON Data Skills. NEON, n.d. Web. 08 Mar. 2017. Dumoulin, Vincent. "A Guide to Convolution Arithmetic for Deep Learning." (n.d.): n. pag. 24 Mar. 2016. Web. 8 Mar. 2017.