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rivamap

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an automated river analysis and mapping engine

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# RivaMap: An Automated River Analysis and Mapping Engine
[![Build Status](https://travis-ci.com/elbeejay/rivamap.svg?branch=master)](https://travis-ci.com/elbeejay/rivamap) [![Coverage Status](https://coveralls.io/repos/github/elbeejay/rivamap/badge.svg?branch=master)](https://coveralls.io/github/elbeejay/rivamap?branch=master)
## Related papers
* F. Isikdogan, A.C. Bovik, and P. Passalacqua, "RivaMap: an automated river analysis and mapping engine," *Remote Sensing of Environment, Special Issue on Big Remotely Sensed Data*, 2017. [[**Read at ScienceDirect**]](http://www.sciencedirect.com/science/article/pii/S0034425717301475), [[**PDF**]](http://www.isikdogan.com/files/isikdogan2017_rivamap.pdf)
* F. Isikdogan, A.C. Bovik, and P. Passalacqua, "Automatic channel network extraction from remotely sensed images by singularity analysis," *IEEE Geoscience and Remote Sensing Letters*, 12, 11, 2218-2221, 2015. [[**Read at IEEExplore**]](http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7192616), [[**PDF**]](http://live.ece.utexas.edu/publications/2015/Isikdogan_GRSL_2015_Channel_Network_Extraction.pdf)

## Dependencies and Installation
**Dependencies:**
* OpenCV 2.4
* Python 3.x
* Numpy
* Scipy
* Matplotlib
* GDAL
* pyshp

**Installing from PyPI:**

$ sudo pip install rivamap

**Installing from GitHub:**

$ git clone https://github.com/isikdogan/rivamap.git
    $ sudo python setup.py install

**Example Use:**

See [example.ipynb](./examples/example.ipynb)

## Example Results

## Reference

<table>
 <tbody>
 <tr>
 <th>
 
 Function
 
 </th>
 <th>
 
 Description
 
 </th>
 </tr>
 <tr>
 <td>
 
 preprocess.mndwi
 
 </td>
 <td>
 
 Computes the modified normalized difference water index.
 
 
 Inputs:
 
 green: green band (e.g. Landsat 8 band 3) 
 mir: middle infrared band (e.g. Landsat 8 band 6)
 
 
 Returns:
 
 mndwi: mndwi response
 
 </td>
 </tr>
 <tr>
 <td>
 
 preprocess.contrastStretch
 
 </td>
 <td>
 
 Applies contrast stretch to an input image. Inputs and outputs an image.
 
 </td>
 </tr>
 <tr>
 <td>
 
 preprocess.im2double
 
 </td>
 <td>
 
 Converts image datatype to float. Inputs and outputs an image.
 
 </td>
 </tr>
 <tr>
 <td>
 
 preprocess.double2im
 
 </td>
 <td>
 
 Converts double data array to image. Inputs and outputs an image.
 
 </td>
 </tr>
 <tr>
 <td>
 
 singularity_index.SingularityIndexFilters
 
 </td>
 <td>
 
 Creates the filters that are needed for computing the modified multiscale singularity index response. The filters can be used for processing many input images once the filters are created.
 
 
 Keyword arguments: 
 minScale: minimum scale sigma (default 1.2 pixels) 
 nrScales: number of scales (default 15)
 
 </td>
 </tr>
 <tr>
 <td>
 
 singularity_index.applyMMSI
 
 </td>
 <td>
 
 Applies the filters to a given input image to compute the modified multiscale singularity index response. Estimates the width and the dominant orientation angle for each spatial location.
 
 
 Inputs: 
 I1: input image (e.g. Landsat NIR band or MNDWI) 
 filters: an instance of SingularityIndexFilters class that contains precomputed filters 
 togglePolarity: changes polarity, use if the rivers are darker than land in the input image (i.e. SAR images)
 
 
 Returns: 
 psi: the singularity index response 
 widthMap: estimated width at each spatial location (x,y) 
 orient: local orientation at each spatial location (x,y)
 
 </td>
 </tr>
 <tr>
 <td>
 
 delineate.extractCenterlines
 
 </td>
 <td>
 
 Uses the previously computed singularity index response (psi) and the dominant orientation (orient) to extract centerlines.
 
 
 Inputs: (can be obtained by running applyMMSI function) 
 psi: the singularity index response 
 orient: local orientation at each spatial location (x,y)
 
 
 Returns: 
 nms: Non-maxima suppressed singularity index response (centerlines)
 
 </td>
 </tr>
 <tr>
 <td>
 
 delineate.thresholdCenterlines
 
 </td>
 <td>
 
 Uses a continuity-preserving hysteresis thresholding to classify centerlines.
 
 
 Inputs: 
 nms: Non-maxima suppressed singularity index response
 
 
 Keyword Arguments: 
 bimodal: true if the areas of rivers in the image are sufficiently large that the distribution of psi is bimodal 
 tLow: lower threshold (automatically set if bimodal=True) 
 tHigh: higher threshold (automatically set if bimodal=True)
 
 
 Returns: 
 centerlines: a binary matrix that indicates centerline locations
 
 </td>
 </tr>
 <tr>
 <td>
 
 georef.loadGeoMetadata
 
 </td>
 <td>
 
 Reads metadata from a GeoTIFF file.
 
 
 Inputs: 
 filepath: the path to the file
 
 
 Returns: 
 gm: metadata
 
 </td>
 </tr>
 <tr>
 <td>
 
 georef.saveAsGeoTiff
 
 </td>
 <td>
 
 Saves a raster image as a GeoTIFF file
 
 
 Inputs: 
 gm: georeferencing metadata 
 I: raster image 
 filepath: save destination
 
 </td>
 </tr>
 <tr>
 <td>
 
 georef.pix2lonlat
 
 </td>
 <td>
 
 Convers pixel coordinates into longitude and latitude.
 
 
 Inputs: 
 gm: georeferencing metadata 
 x, y: pixel coordinates
 
 
 Returns: 
 lon, lat: longitude and latitude
 
 </td>
 </tr>
 <tr>
 <td>
 
 georef.lonlat2pix
 
 </td>
 <td>
 
 Convers longitude and latitude into pixel coordinates.
 
 
 Inputs: 
 gm: georeferencing metadata 
 lon, lat: longitude and latitude
 
 
 Returns: 
 x, y: pixel coordinates
 
 </td>
 </tr>
 <tr>
 <td>
 
 georef.exportCSVfile
 
 </td>
 <td>
 
 Exports (coordinate, width) pairs to a comma separated text file.
 
 
 Inputs: 
 centerlines: a binary matrix that indicates centerline locations 
 widthMap: estimated width at each spatial location (x,y) 
 gm: georeferencing metadata filepath: the path to the file
 
 </td>
 </tr>
	<tr>
 <td>
 
 georef.exportShapeFile [NEW]
 
 </td>
 <td>
 
 Exports line segments to a ShapeFile.
 
 
 Inputs: 
 centerlines: a binary matrix that indicates centerline locations 
 widthMap: estimated width at each spatial location (x,y) 
 gm: georeferencing metadata filepath: the path to the file
 
 </td>
 </tr>
 <tr>
 <td>
 
 visualization.generateRasterMap
 
 </td>
 <td>
 
 Generates a raster map of channels. It draws a line of length w(x, y) and orientation &theta;(x, y) at each spatial location.
 
 
 Inputs: 
 centerlines: a binary matrix that indicates centerline locations 
 orient: local orientation at each spatial location (x,y) 
 widthMap: estimated width at each spatial location (x,y)
 
 
 Keyword Arguments: 
 thickness: thickness of the lines (default 3)
 
 
 Returns: 
 raster: the raster map
 
 </td>
 </tr>
 <tr>
 <td>
 
 visualization.generateVectorMap
 
 </td>
 <td>
 
 Generates a vector map of channels. It draws a line of length w(x, y) and orientation &theta;(x, y) at each spatial location.
 
 
 Inputs: 
 centerlines: a binary matrix that indicates centerline locations 
 orient: local orientation at each spatial location (x,y) 
 widthMap: estimated width at each spatial location (x,y) 
 saveDest: output figure save destination
 
 
 Keyword Arguments: 
 thickness: thickness of the lines (default 0.2)
 
 
 Returns: 
 None (saves the figure at saveDest)
 
 </td>
 </tr>
 <tr>
 <td>
 
 visualization.quiverPlot
 
 </td>
 <td>
 
 Generates a quiver plot that shows channel orientation and singularity index response strength.
 
 
 Inputs: 
 psi: singularity index response 
 orient: local orientation at each spatial location (x,y) 
 saveDest: output figure save destination
 
 
 Returns: 
 None (saves the figure at saveDest)
 
 </td>
 </tr>
 </tbody>
</table>

rivamap

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