JackWoo0831 / MSTAR_Kmeans_SVM

MSTAR dataset. Unoffical python implementation of Paper: Bag-of-Visual-Words Based Feature Extraction for SAR Target Classification

MSTAR_Kmeans_SVM

MSTAR dataset. Unoffical python implementation of Paper: Bag-of-Visual-Words Based Feature Extraction for SAR Target Classification

MSTAR数据集的分类, 复现论文Bag-of-Visual-Words Based Feature Extraction for SAR Target Classification

Performance:

trian dataset acc: 90.45%
test dataset acc: 85.37%

Principles:

The train steps are as follow:

1. gamma transform and extract HOG features
2. use K-means cluster to build a codebook(Bag of word, that is some feature vectors can represent origin train data.) and save it
3. use the Eculid distance between HOG feature and codebooks to encoder a new feature
4. train a Linear SVM model and save it
5. calculate the acc

The test steps are as follow:

1. gamma transform and extract HOG features
2. use the Eculid distance between HOG feature and codebooks to encoder a new feature
3. SVM prediction
4. calculate the acc

How to use

1. Organizate the dataset

Download the dataset:

Link：https://pan.baidu.com/s/1Sa0AC9aERD36fpXGGYuI-w Code：u4zg

unzip the JPEG-e.7z, and it should look something like this:

The first two folders are for the mixed type targets, the third and fourth folders are for the data of the T72 variant targets, and the last folder contains the T-72, BMP-2, BTR-70, SLICY targets. This repo focuses on the targets in the first two folders. According to the official recommendation, the images from the 17-degree imaging side view are used as the training set, and the images from the 15-degree imaging side view are used as the test set. It consists of 8 kinds of objectives, namely BTR_60, 2S1, BRDM_2, D7, SLICY, T62, ZIL131, ZSU_23_4.

For convenience, all targets of 17-degree imaging are stored in the train folder, and all targets of 15-degree imaging are stored in the test folder:

2. train

this repo relies on sklearn, opencv and numpy. you can run

pip install -r requirements.txt

to install.

then run train.py directly or with some arguments:

python train.py 

or

python train.py --data_root "D:/data/MSTAR" --cluster_num 64 --ratio 0.75

3. test

Same as train: just run

python test.py 

or

python test.py --data_root "D:/data/MSTAR" --cluster_num 64 --ratio 0.75