EarthMapper - GitLite

KO | EN

gitlite — search

#typescript #go #golang #android #machine-learning #rust #deep-learning #macos #nodejs #awesome #vue #security #pytorch #openai

EarthMapper

(★ 98)

Pipeline for the Semantic Segmentation (i.e., classification) of Remote Sensing Imagery

파일 탐색기

최종 버전 다운로드 (.zip)

- __init__.py
- mlp.py
- rf_workflow.py
- ssmlp.py
- svm_cv_workflow.py
- svm_workflow.py
- example_pipeline.py
- example_scae_svm.py
- __init__.py
- cae.py
- mcae.py
- scae.py
- __init__.py
- aviris.py
- aviris_bands.hdr
- gliht.py
- hyperion.py
- hyperion_bands.csv
- utils.py
- __init__.py
- convolution.py
- train_val_split.py
- utils.py
.gitignore
__init__.py
metrics.py
pipeline.py
postprocessing.py
preprocessing.py
README.md
setup.py
tf_initializers.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/rmkemker/EarthMapper

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd EarthMapper

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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.

<p align="center">
<img  src="http://www.cis.rit.edu/~rmk6217/img/earthmapper.png" width="200">
</p>

## 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](https://www.anaconda.com/download/))
* numpy, scipy, and matplotlib
* [scikit-learn](http://scikit-learn.org/stable/)
* [spectral python](http://www.spectralpython.net/)
* [gdal](http://www.gdal.org/)
* [tensorflow](https://www.tensorflow.org/)
* [pydensecrf](https://github.com/lucasb-eyer/pydensecrf)
* [gco_python](https://github.com/amueller/gco_python)

## Instructions ##

### Installation ###
```console
$ python setup.py
```
### Run example ###
```console
$ 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
](https://arxiv.org/abs/1804.00292).  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](https://arxiv.org/abs/1803.09824). IEEE Transactions on Geoscience and Remote Sensing (TGRS).
* Kemker, R., Kanan C. (2017) [Self-Taught Feature Learning for Hyperspectral Image Classification](http://ieeexplore.ieee.org/document/7875467/). 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](https://arxiv.org/abs/1801.08268), 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](http://ieeexplore.ieee.org/abstract/document/8071716/), 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/