# SAR.AI

**Repository Path**: chenlei2013/SAR.AI

## Basic Information

- **Project Name**: SAR.AI
- **Description**: :satellite: Multi-Level Pattern Histogram for Synthetic-Aperture Radar (SAR) image classification into terrain classes.
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 2
- **Created**: 2019-06-16
- **Last Updated**: 2024-04-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SAR Image Classification with MLPH

Modified Multi-Level Pattern Histograms (MLPH) for SAR image classification.

Some results for different regions in and around New York City area:

![](./report_images/34.png)

![](./report_images/40.png)

![](./report_images/33.png)

## Dependencies

Platform tested on: **Ubuntu 16.04**

Softwares/Packages required:
- Python >2.6
- pickle
- numpy
- sklearn
- skimage
- scipy


## Instructions to Run the Application

- Open the terminal inside the directory “IP_Project_Group23”

- To run MLPH on training images and train SVM & test on hold-out set:

$ python -m sarclf -run_mlph 1 -train_svm 1

- To run pretrained SVM on test image pixels:

$ python -m sarclf

- To run pretrained SVM to classify an image:

$ python -m sarclf -test 0 -clfimg <image_path>

- For example, for image 2.tif in test_images folder, use:

$ python -m sarclf -test 0 -clfimg ./test_images/2.tif

- To do any of the above steps with our modified MLPH: Add “-modified 1”

Example:

$ python -m sarclf -test 0 -clfimg ./test_images/2.tif -modified 1



## Methodology

Following the research on MLPH, we derive a pattern matrix for each pixel based on a threshold value, use these matrices varying bin lengths to get local pattern sub-histograms, concatenated to give local pattern histogram. With multiple thresholds, the concatenation of local pattern histograms gives MLPH for each pixel.

![](./report_images/1.png)

![](./report_images/2.png)

![](./report_images/3.png)