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https://github.com/jgrss/mtlchmm
Multi-temporal land cover maps with a Hidden Markov Model
https://github.com/jgrss/mtlchmm
Last synced: about 2 months ago
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Multi-temporal land cover maps with a Hidden Markov Model
- Host: GitHub
- URL: https://github.com/jgrss/mtlchmm
- Owner: jgrss
- License: mit
- Created: 2017-08-01T16:44:57.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2021-05-11T00:37:28.000Z (over 3 years ago)
- Last Synced: 2024-04-24T12:05:21.511Z (5 months ago)
- Language: Python
- Size: 84 KB
- Stars: 14
- Watchers: 3
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
[](#mit-license)[](#python-3.6)[](#package-version)
[](https://lbesson.mit-license.org/)
[](https://www.python.org/downloads/release/python-360/)
Multi-temporal land cover maps with a Hidden Markov Model (MTLCHMM)
---
This is largely a port of S. Parker Abercrombie's code. The main differences between the
original code and this library are:
1. MTLCHMM is designed to support any sensor.
2. Parallel processing at the pixel level is handled differently (however, no tests
between the two have been conducted).
## Reference
> Abercrombie, S Parker and Friedl, Mark A (2016) [Improving the Consistency of Multitemporal Land
Cover Maps Using a Hidden Markov Model](https://ieeexplore.ieee.org/document/7254169/). _IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING_, 54(2), 703--713.
## New in v. 0.1.0:
* Image I/O is now handled with [`Rasterio`](https://rasterio.readthedocs.io/en/stable/).
### Usage
```python
>>> import mtlchmm
>>>
>>> hmm_model = mtlchmm.MTLCHMM(method='forward-backward',
>>> transition_prior=0.1,
>>> out_dir='hmm',
>>> block_size=512,
>>> n_jobs=-1,
>>> tiled=True,
>>> blockxsize=512,
>>> blockysize=512,
>>> compress='lzw')
>>>
>>> hmm_model.fit_predict(['lc_probas_yr01.tif',
>>> 'lc_probas_yr02.tif',
>>> 'lc_probas_yr03.tif'])
```
```text
Results from the above example would be written to:
/lc_probas_yr01_hmm.tif
/lc_probas_yr02_hmm.tif
/lc_probas_yr03_hmm.tif
```
### Full example with classification using [MpGlue](https://github.com/jgrss/mpglue)
```python
>>> import mpglue as gl
>>> import mtlchmm
>>>
>>> cl = gl.classification()
>>>
>>> # Load land cover samples.
>>> cl.split_samples('/samples.txt')
>>>
>>> # Train a Random Forest classification model.
>>> cl.construct_model(classifier_info={'classifier': 'RF',
>>> 'trees': 1000,
>>> 'max_depth': 25})
>>>
>>> lc_probabilities = list()
>>>
>>> # Predict class conditional probabilities and write to file.
>>> for im in ['yr01.tif', 'yr02.tif', 'yr03.tif']:
>>>
>>> out_probs = '{}_probas.tif'.format(im.split('.')[0])
>>>
>>> cl.predict(im,
>>> out_probs,
>>> predict_probs=True)
>>>
>>> # Store output file names.
>>> lc_probabilities.append(out_probs)
>>>
>>> # Get the class transitional probabilities.
>>> hmm_model = mtlchmm.MTLCHMM(method='forward-backward',
>>> transition_prior=0.1,
>>> n_jobs=-1)
>>>
>>> # Fit the HMM model and write
>>> # adjusted probabilities to file.
>>> hmm_model.fit_predict(lc_probabilities)
```
Installation
---
### Dependencies
* NumPy
* Rasterio
#### Install from GitHub
```commandline
> pip install git+https://github.com/jgrss/mtlchmm.git
```
#### Alternatively, clone the latest version
```commandline
> git clone https://github.com/jgrss/mtlchmm.git
> cd mtlchmm/
> python setup.py install
```
#### Updating
```commandline
> cd mtlchmm/
> git pull origin master
> python setup.py install
```