EarthMapper

Project repository for EarthMapper. This is a toolbox for the semantic segmentation of non-RGB (i.e., multispectral/hyperspectral) imagery. We will work on adding more examples and better documentation.

Description

This is a classification pipeline from various projects that we have worked on over the past few years. Currently available options include:

Pre-Processing

• MinMaxScaler - Scale data (per-channel) between a given feature range (e.g., 0-1)
• StandardScaler - Scale data (per-channel) to zero-mean/unit-variance
• PCA - Reduce dimensionality via principal component analysis
• Normalize - Scale data using the per-channel L2 norm

Spatial-Spectral Feature Extraction

• Stacked Convolutional Autoencoder (SCAE)
• Stacked Multi-Loss Convolutional Autoencoder (SMCAE)

Classifiers

• SVMWorkflow - Support vector machine with a given training/validation split
• SVMCVWorkflow - Support vector machine that uses n-fold cross-validation to find optimal hyperparameters
• RandomForestWorkflow - Random Forest classifier
• MLP - Multi-layer Perceptron Neural Network classifier
• SSMLP - Semi-supervised MLP Neural Network classifier

Post-Processors

• Markov Random Field (MRF)
• Fully-Connected Conditional Random Field (CRF)

Dependencies

• Python 3.5 (We recommend the Anaconda Python Distribution)
• numpy, scipy, and matplotlib
• scikit-learn
• spectral python
• gdal
• tensorflow
• pydensecrf
• gco_python

Instructions

Installation

$ python setup.py

Run example

$ python examples/example_pipeline.py

Citations

If you use our product, please cite:

• Kemker, R., Gewali, U. B., Kanan, C. EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery . In review at the IEEE Geoscience and Remote Sensing Letters (GRSL).
• Kemker, R., Luu, R., and Kanan C. (2018) Low-Shot Learning for the Semantic Segmentation of Remote Sensing Imagery. IEEE Transactions on Geoscience and Remote Sensing (TGRS).
• Kemker, R., Kanan C. (2017) Self-Taught Feature Learning for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing (TGRS), 55(5): 2693-2705. 10.1109/TGRS.2017.2651639
• U. B. Gewali and S. T. Monteiro, A tutorial on modeling and inference in undirected graphical models for hyperspectral image analysis, In review at the International Journal of Remote Sensing (IJRS).
• U. B. Gewali and S. T. Monteiro, Spectral angle based unary energy functions for spatial-spectral hyperspectral classification using Markov random fields, in Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Los Angeles, CA, USA, Aug. 2016.

Points of Contact

• Ronald Kemker - http://www.cis.rit.edu/~rmk6217/
• Utsav Gewali - http://www.cis.rit.edu/~ubg9540/
• Chris Kanan - http://www.chriskanan.com/

Also Check Out

• RIT-18 Dataset - https://github.com/rmkemker/RIT-18
• Machine and Neuromorphic Perception Laboratory - http://klab.cis.rit.edu/