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https://github.com/felixriese/CNN-SoilTextureClassification
1-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data
https://github.com/felixriese/CNN-SoilTextureClassification
1d-cnn classification cnn conference convolutional-neural-networks hyperspectral-data publication publication-code soil-texture-classification
Last synced: 3 months ago
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1-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data
- Host: GitHub
- URL: https://github.com/felixriese/CNN-SoilTextureClassification
- Owner: felixriese
- License: mit
- Created: 2019-01-15T11:28:31.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-05-09T16:39:41.000Z (over 2 years ago)
- Last Synced: 2024-06-11T17:13:39.220Z (5 months ago)
- Topics: 1d-cnn, classification, cnn, conference, convolutional-neural-networks, hyperspectral-data, publication, publication-code, soil-texture-classification
- Language: Python
- Homepage: https://doi.org/10.5281/zenodo.2540718
- Size: 32.2 KB
- Stars: 55
- Watchers: 4
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
- open-sustainable-technology - CNN-SoilTextureClassification - One-dimensional convolutional neural networks (CNN) for the classification of soil texture based on hyperspectral data. (Natural Resources / Soil and Land)
README
[](https://travis-ci.com/felixriese/CNN-SoilTextureClassification)
[](https://codecov.io/gh/felixriese/CNN-SoilTextureClassification)
[](https://www.codacy.com/manual/felixriese/CNN-SoilTextureClassification?utm_source=github.com&utm_medium=referral&utm_content=felixriese/CNN-SoilTextureClassification&utm_campaign=Badge_Grade)
[](https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W5/615/2019/)
[](LICENSE)
# CNN Soil Texture Classification
1-dimensional convolutional neural networks (CNN) for the classification of
soil texture based on hyperspectral data.
## Description
We present 1-dimensional (1D) convolutional neural networks (CNN) for the
classification of soil texture based on hyperspectral data. The following CNN
models are included:
* `LucasCNN`
* `LucasResNet`
* `LucasCoordConv`
* `HuEtAl`: 1D CNN by Hu et al. (2015), DOI: [10.1155/2015/258619](http://dx.doi.org/10.1155/2015/258619)
* `LiuEtAl`: 1D CNN by Liu et al. (2018), DOI: [10.3390/s18093169](https://dx.doi.org/10.3390%2Fs18093169)
These 1D CNNs are optimized for the soil texture classification based on the hyperspectral data of the *Land Use/Cover Area Frame Survey* (LUCAS) topsoil dataset. It is available [here](https://esdac.jrc.ec.europa.eu/projects/lucas). For more information have a look in our publication (see below).
**Introducing paper:** [arXiv:1901.04846](https://arxiv.org/abs/1901.04846)
**Licence:** [MIT](LICENSE)
**Authors:**
* [Felix M. Riese](mailto:[email protected])
* [Sina Keller](mailto:[email protected])
**Citation of the code and the paper:** see [below](#citation) and in the [bibtex](bibliography.bib) file
## Requirements
* see [Dockerfile](Dockerfile)
* download `coord.py` from [titu1994/keras-coordconv](https://github.com/titu1994/keras-coordconv) based on [arXiv:1807.03247](https://arxiv.org/abs/1807.03247)
## Setup
```bash
git clone https://github.com/felixriese/CNN-SoilTextureClassification.git
cd CNN-SoilTextureClassification/
wget https://raw.githubusercontent.com/titu1994/keras-coordconv/c045e3f1ff7dabd4060f515e4b900263eddf1723/coord.py .
```
## Usage
You can import the Keras models like that:
```python
import cnn_models as cnn
model = cnn.getKerasModel("LucasCNN")
model.compile(...)
```
Example code is given in the `lucas_classification.py`. You can use it like that:
```python
from lucas_classification import lucas_classification
score = lucas_classification(
data=[X_train, X_val, y_train, y_val],
model_name="LucasCNN",
batch_size=32,
epochs=200,
random_state=42)
print(score)
```
## Citation
[1] F. M. Riese, "CNN Soil Texture Classification",
[DOI:10.5281/zenodo.2540718](https://doi.org/10.5281/zenodo.2540718), 2019.
[](https://doi.org/10.5281/zenodo.2540718)
```tex
@misc{riese2019cnn,
author = {Riese, Felix~M.},
title = {{CNN Soil Texture Classification}},
year = {2019},
publisher = {Zenodo},
DOI = {10.5281/zenodo.2540718},
}
```
## Code is Supplementary Material to
[2] F. M. Riese and S. Keller, "Soil Texture Classification with 1D
Convolutional Neural Networks based on Hyperspectral Data", ISPRS Annals of
Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. IV-2/W5,
pp. 615-621, 2019. [DOI:10.5194/isprs-annals-IV-2-W5-615-2019](https://doi.org/10.5194/isprs-annals-IV-2-W5-615-2019)
```tex
@article{riese2019soil,
author = {Riese, Felix~M. and Keller, Sina},
title = {Soil Texture Classification with 1D Convolutional Neural Networks based on Hyperspectral Data},
year = {2019},
journal = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences},
volume = {IV-2/W5},
pages = {615--621},
doi = {10.5194/isprs-annals-IV-2-W5-615-2019},
}
```
[3] F. M. Riese, "LUCAS Soil Texture Processing Scripts," Zenodo, 2020.
[DOI:0.5281/zenodo.3871431](https://doi.org/10.5281/zenodo.3871431)
[4] Felix M. Riese. "Development and Applications of Machine Learning Methods
for Hyperspectral Data." PhD thesis. Karlsruhe, Germany: Karlsruhe Institute of
Technology (KIT), 2020. [DOI:10.5445/IR/1000120067](https://doi.org/10.5445/IR/1000120067)