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.
This is a classification pipeline from various projects that we have worked on over the past few years. Currently available options include:
- 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
- Stacked Convolutional Autoencoder (SCAE)
- Stacked Multi-Loss Convolutional Autoencoder (SMCAE)
- 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
- Markov Random Field (MRF)
- Fully-Connected Conditional Random Field (CRF)
- Python 3.5 (We recommend the Anaconda Python Distribution)
- numpy, scipy, and matplotlib
- scikit-learn
- spectral python
- gdal
- tensorflow
- pydensecrf
- gco_python
$ python setup.py$ python examples/example_pipeline.pyIf 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.
- Ronald Kemker - http://www.cis.rit.edu/~rmk6217/
- Utsav Gewali - http://www.cis.rit.edu/~ubg9540/
- Chris Kanan - http://www.chriskanan.com/
- RIT-18 Dataset - https://github.com/rmkemker/RIT-18
- Machine and Neuromorphic Perception Laboratory - http://klab.cis.rit.edu/