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https://github.com/awesome-spectral-indices/SpectralIndices.jl

Awesome Spectral Indices in Julia
https://github.com/awesome-spectral-indices/SpectralIndices.jl

List: SpectralIndices.jl

gis landsat modis remote-sensing satellite-imagery sentinel spectral spectral-dataset spectral-index spectral-indices

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Awesome Spectral Indices in Julia

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README 

        
# SpectralIndices.jl





    




| **Documentation** | **Build Status** | **Julia** | **Testing** |

|:-----------------:|:----------------:|:---------:|:---------|

| [![docs][docs-img]][docs-url] | [![CI][ci-img]][ci-url] [![codecov][cc-img]][cc-url] | [![Julia][julia-img]][julia-url] [![Code Style: Blue][style-img]][style-url] | [![Aqua QA][aqua-img]][aqua-url] |



[docs-img]: https://img.shields.io/badge/docs-stable-blue.svg

[docs-url]: https://awesome-spectral-indices.github.io/SpectralIndices.jl/dev/



[ci-img]: https://github.com/awesome-spectral-indices/SpectralIndices.jl/actions/workflows/CI.yml/badge.svg

[ci-url]: https://github.com/awesome-spectral-indices/SpectralIndices.jl/actions/workflows/CI.yml



[cc-img]: https://codecov.io/gh/awesome-spectral-indices/SpectralIndices.jl/graph/badge.svg?token=0IRRDAZM0U

[cc-url]: https://codecov.io/gh/awesome-spectral-indices/SpectralIndices.jl



[julia-img]: https://img.shields.io/badge/julia-v1.9+-blue.svg

[julia-url]: https://julialang.org/



[style-img]: https://img.shields.io/badge/code%20style-blue-4495d1.svg

[style-url]: https://github.com/invenia/BlueStyle



[aqua-img]: https://raw.githubusercontent.com/JuliaTesting/Aqua.jl/master/badge.svg

[aqua-url]: https://github.com/JuliaTesting/Aqua.jl



[jet-img]: https://img.shields.io/badge/%E2%9C%88%EF%B8%8F%20tested%20with%20-%20JET.jl%20-%20red

[jet-url]: https://github.com/aviatesk/JET.jl



## Overview 📖



SpectralIndices.jl is a Julia package for working with spectral indices commonly used in remote sensing and earth observation applications. It provides a convenient way to compute various spectral indices using Julia's high-performance capabilities.



## Features 🚀



- Computation for a wide range of spectral indices 

- Computation support for `Float64`, `Float32` and `Float16` at index formula level to support fast computation for ML applications

- Support for various data types, including but not limited to (see this [issue](https://github.com/awesome-spectral-indices/SpectralIndices.jl/issues/8) for an updated list and WIP)

  - [x] Arrays

  - [x] DataFrames

  - [x] YAXArrays

- Flexible parameter input options

- Compatibility with multiple remote sensing platforms and sensors



## Installation 💻

SpectralIndices.jl is registered in the general registry, please install it using the following:

```julia_repl

julia> ]

pkg> add SpectralIndices

```

or

```julia

using Pkg

Pkg.add("SpectralIndices")

```



## Usage 🛠️



You can compute spectral indices either by specifying the index and its parameters or using predefined SpectralIndex instances.



### Using `compute_index`

```julia

using SpectralIndices

using DataFrames



# Compute NDVI with direct parameter input

result = compute_index("NDVI", N = 0.643, R = 0.175)



# Compute NDVI with direct parameter input and direct NDVI call

result = compute_index(NDVI, N = 0.643, R = 0.175)



# Compute multiple indices with array inputs

multi_result = compute_index(["NDVI", "SAVI"], N = fill(0.643, 5), R = fill(0.175, 5), L = fill(0.5, 5))



# use Dataframes

df = DataFrame(N = [0.643, 0.560], R = [0.175, 0.225])

result_df_single = compute_index("NDVI", df)

# Multiple inputs in a dataframe

df = DataFrame(N = [0.643, 0.560], R = [0.175, 0.225], L = [0.5, 0.5])

result_df_multiple = compute_index(["NDVI", "SAVI"], df)

```



To compute at custom Float precision input the bands at the chosen precision and specify the Float type to the `compute_index` function

```julia

# Compute NDVI with direct parameter input

result = compute_index(Float32, "NDVI", N = Float32(0.643), R = Float32(0.175))

```



### Using `compute`

```julia

# Define a SpectralIndex instance



# Compute using the instance with keyword arguments

result = compute(NDVI, N = 0.643, R = 0.175) #NDVI is autmatically in namespace with the import of SpectraIndices.jl



# Compute with array inputs

array_result = compute(NDVI, N = fill(0.643, (5, 5)), R = fill(0.175, (5, 5)))

```



For more advanced usage and detailed documentation, please refer to the documentation.



## Code Structure



![fig1](https://github.com/awesome-spectral-indices/SpectralIndices.jl/assets/10376688/fb680f5a-7130-49fc-ae20-386fed3777ed)



## Contributing 🤝



Contributions to SpectralIndices.jl are welcome! If you would like to contribute, please see our Contribution Guidelines for more information.



## License 📜



SpectralIndices.jl is licensed under the MIT License. See [LICENSE](https://github.com/awesome-spectral-indices/SpectralIndices.jl/blob/main/LICENSE) for more information.



## Acknowledgments ✨



This package is inspired by the [Spyndex](https://github.com/awesome-spectral-indices/spyndex) Python library for spectral indices. The logo is AI-generated by dalle3 through ChatGPT and modified by the talented [David Montero](https://github.com/davemlz).



## Support 🆘



If you have any questions, issues, or feature requests, please open an issue or contact us via email.



## Citation 🔗



If you use SpectralIndices.jl in your research, please consider citing it using the following DOI:



```bibtex

@article{montero2023standardized,

  title={A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research},

  author={Montero, David and Aybar, C{\'e}sar and Mahecha, Miguel D and Martinuzzi, Francesco and S{\"o}chting, Maximilian and Wieneke, Sebastian},

  journal={Scientific Data},

  volume={10},

  number={1},

  pages={197},

  year={2023},

  publisher={Nature Publishing Group UK London}

}

```

[![RSC4Earth](https://github.com/davemlz/sen2nbar/raw/main/docs/_static/esds.png)](https://rsc4earth.de/authors/esds/)