AConvNet

Target Classification Using the Deep Convolutional Networks for SAR Images

This repository is reproduced-implementation of AConvNet which recognize target from MSTAR dataset. You can see the official implementation of the author at MSTAR-AConvNet.

Dataset

MSTAR (Moving and Stationary Target Acquisition and Recognition) Database

Format

Header
- Type: ASCII
- Including data shape(width, height), serial number, azimuth angle, etc.
Data
- Type: Two-bytes
- Shape: W x H x 2
  - Magnitude block
  - Phase Block

Below figure is the example of magnitude block(Left) and phase block(Right)

Model

The proposed model only consists of sparsely connected layers without any fully connected layers.

It eases the over-fitting problem by reducing the number of free parameters(model capacity)

layer	Input	Conv 1	Conv 2	Conv 3	Conv 4	Conv 5
channels	2	16	32	64	128	10
weight size	-	5 x 5	5 x 5	6 x 6	5 x 5	3 x 3
pooling	-	2 x 2 - s2	2 x 2 - s2	2 x 2 - s2	-	-
dropout	-	-	-	-	0.5	-
activation	linear	ReLU	ReLU	ReLU	ReLU	Softmax

Training

For training, this implementation fixes the random seed to 12321 for reproducibility.

The experimental conditions are same as in the paper, except for data augmentation. You can see the details in src/model/_base.py and experiments/config/AConvNet-SOC.json

Data Augmentation

The author uses random shifting to extract 88 x 88 patches from 128 x 128 SAR image chips.
- The number of training images per one SAR image chip could be increased at maximum by (128 - 88 + 1) x (128 - 88 + 1) = 1681.
However, for SOC, this repository does not use random shifting tue to accuracy issue.
- You can see the details in src/data/generate_dataset.py and src/data/mstar.py
- The implementation details for data augmentation is as:
  - Crop the center of 94 x 94 size image on 100 x 100 SAR image chip (49 patches per image chip).
  - Extract 88 x 88 patches with stride 1 from 94 x 94 image with random cropping.

Experiments

You can download the MSTAR Dataset from MSTAR Overview

MSTAR Clutter - CD1 / CD2
MSTAR Target Chips (T72 BMP2 BTR70 SLICY) - CD1
MSTAR / IU Mixed Targets - CD1 / CD2
MSTAR / IU T-72 Variants - CD1 / CD2
MSTAR Predictlite Software - CD1

You can download the experimental results of this repository from experiments

Standard Operating Condition (SOC)

		Train		Test
Class	Serial No.	Depression	No. Images	Depression	No. Images
BMP-2	9563	17	233	15	196
BTR-70	c71	17	233	15	196
T-72	132	17	232	15	196
BTR-60	k10yt7532	17	256	15	195
2S1	b01	17	299	15	274
BRDM-2	E-71	17	298	15	274
D7	92v13015	17	299	15	274
T-62	A51	17	299	15	273
ZIL-131	E12	17	299	15	274
ZSU-234	d08	17	299	15	274

Training Set (Depression: 17$\degree$)

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TRAIN/17_DEG
│    ├ BMP2/SN_9563/*.000 (233 images)
│    ├ BTR70/SN_C71/*.004 (233 images)
│    └ T72/SN_132/*.015   (232 images)
└ ...

MSTAR-PublicMixedTargets-CD2/MSTAR_PUBLIC_MIXED_TARGETS_CD2
├ 17_DEG
│    ├ COL1/SCENE1/BTR_60/*.003  (256 images)
│    └ COL2/SCENE1
│        ├ 2S1/*.000            (299 images)
│        ├ BRDM_2/*.001         (298 images)
│        ├ D7/*.005             (299 images)
│        ├ SLICY
│        ├ T62/*.016            (299 images)
│        ├ ZIL131/*.025         (299 images)
│        └ ZSU_23_4/*.026       (299 images)
└ ...

Test Set (Depression: 15$\degree$)

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TEST/15_DEG
│    ├ BMP2/SN_9563/*.000 (195 images)
│    ├ BTR70/SN_C71/*.004 (196 images)
│    └ T72/SN_132/*.015   (196 images)
└ ...

MSTAR-PublicMixedTargets-CD1/MSTAR_PUBLIC_MIXED_TARGETS_CD1
├ 15_DEG
│    ├ COL1/SCENE1/BTR_60/*.003  (195 images)
│    └ COL2/SCENE1
│        ├ 2S1/*.000            (274 images)
│        ├ BRDM_2/*.001         (274 images)
│        ├ D7/*.005             (274 images)
│        ├ SLICY
│        ├ T62/*.016            (273 images)
│        ├ ZIL131/*.025         (274 images)
│        └ ZSU_23_4/*.026       (274 images)
└ ...

Quick Start Guide for Training

Dataset Preparation

$ cd src/data 
$ python3 generate_dataset.py --is_train=True --use_phase=True --chip_size=100 --patch_size=94 --use_phase=True --dataset=soc 
$ python3 generate_dataset.py --is_train=False --use_phase=True --chip_size=128 --patch_size=128  --use_phase=True --dataset=soc
$ cd ..
$ python3 train.py --config_name=config/AConvNet-SOC.json

Results of SOC

Final Accuracy is 99.34% at epoch 29 (The official accuracy is 99.13%)
You can see the details in notebook/experiments-SOC.ipynb
Visualization of training loss and test accuracy

Confusion Matrix with best model at epoch 28

Noise Simulation [1]
- i.i.d samples from a uniform distribution
- This simulation does not fix the random seed

Noise	1%	5%	10%	15%
AConvNet-PyTorch	98.64	94.10	84.54	71.55
AConvNet-Official	91.76	88.52	75.84	54.68

Extended Operating Conditions (EOC)

EOC-1 (Large depression angle change)

		Train		Test
Class	Serial No.	Depression	No. Images	Depression	No. Images
T-72	A64	17	299	30	196
2S1	b01	17	299	30	274
BRDM-2	E-71	17	298	30	274
ZSU-234	d08	17	299	30	274

Training Set (Depression: 17$\degree$)

MSTAR-PublicT72Variants-CD2/MSTAR_PUBLIC_T_72_VARIANTS_CD2
├ 17_DEG/COL2/SCENE1
│    └ A64/*.024   (299 images)
└ ...

MSTAR-PublicMixedTargets-CD2/MSTAR_PUBLIC_MIXED_TARGETS_CD2
├ 17_DEG
│    └ COL2/SCENE1
│        ├ 2S1/*.000            (299 images)
│        ├ BRDM_2/*.001         (298 images)
│        └ ZSU_23_4/*.026       (299 images)
└ ...

Test Set (Depression: 30$\degree$)

MSTAR-PublicT72Variants-CD2/MSTAR_PUBLIC_T_72_VARIANTS_CD2
├ 30_DEG/COL2/SCENE1
│    └ A64/*.024   (288 images)
└ ...

MSTAR-PublicMixedTargets-CD2/MSTAR_PUBLIC_MIXED_TARGETS_CD2
├ 30_DEG
│    └ COL2/SCENE1
│        ├ 2S1/*.000            (288 images)
│        ├ BRDM_2/*.001         (287 images)
│        ├ ZSU_23_4/*.026       (288 images)
│        └ ...
└ ...

Quick Start Guide for Training

Dataset Preparation
- Download the dataset.zip
- After extracting it, you can find train and test directories inside raw directory.
- Place the two directories (train and test) to the dataset/eoc-1-t72-a64/raw.

$ cd src/data 
$ python3 generate_dataset.py --is_train=True --use_phase=True --chip_size=100 --patch_size=94 --use_phase=True --dataset=eoc-1-t72-a64 
$ python3 generate_dataset.py --is_train=False --use_phase=True --chip_size=128 --patch_size=128  --use_phase=True --dataset=eoc-1-t72-a64
$ cd ..
$ python3 train.py --config_name=config/AConvNet-EOC-1-T72-A64.json

Results of EOC-1

Final Accuracy is 91.49% at epoch 17 (The official accuracy is 96.12%)
You can see the details in notebook/experiments-EOC-1-T72-A64.ipynb
Visualization of training loss and test accuracy

Confusion Matrix with best model at epoch 28

EOC-2 (Target configuration variants)

		Train		Test
Class	Serial No.	Depression	No. Images	Depression	No. Images
BMP-2	9563	17	233	-	-
BRDM-2	E-71	17	298	-	-
BTR-70	c71	17	233	-	-
T-72	132	17	232	-	-
T-72	S7	-	-	15, 17	419
T-72	A32	-	-	15, 17	572
T-72	A62	-	-	15, 17	573
T-72	A63	-	-	15, 17	573
T-72	A64	-	-	15, 17	573

Training Set (Depression: 17$\degree$)

# BMP2, BRDM2, BTR70, T72 are selected from SOC training data

Test Set (Depression: 15$\degree$)

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TRAIN/17_DEG
│    └ T72/SN_S7/*.017   (228 images)
└ ...

MSTAR-PublicT72Variants-CD2/MSTAR_PUBLIC_T_72_VARIANTS_CD2
├ 17_DEG/COL2/SCENE1
│    ├ A32/*.017   (299 images)
│    ├ A62/*.018   (299 images)
│    ├ A63/*.019   (299 images)
│    ├ A64/*.020   (299 images)
├    └ ...
└ ...

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TEST/15_DEG
│    └ T72/SN_S7/*.017   (191 images)
└ ...

MSTAR-PublicT72Variants-CD1/MSTAR_PUBLIC_T_72_VARIANTS_CD1
├ 15_DEG/COL2/SCENE1
│    ├ A32/*.017   (274 images)
│    ├ A62/*.018   (274 images)
│    ├ A63/*.019   (274 images)
│    ├ A64/*.020   (274 images)
├    └ ...
└ ...

Quick Start Guide for Training

Dataset Preparation
- Download the dataset.zip
- After extracting it, you can find train and test directories inside raw directory.
- Place the two directories (train and test) to the dataset/eoc-2-cv/raw.

$ cd src/data 
$ python3 generate_dataset.py --is_train=True --use_phase=True --chip_size=100 --patch_size=94 --use_phase=True --dataset=eoc-2-cv 
$ python3 generate_dataset.py --is_train=False --use_phase=True --chip_size=128 --patch_size=128  --use_phase=True --dataset=eoc-2-cv
$ cd ..
$ python3 train.py --config_name=config/AConvNet-EOC-2-CV.json

Results of EOC-2 Configuration Variants

Final Accuracy is 99.41% at epoch 95 (The official accuracy is 98.93%)
You can see the details in notebook/experiments-EOC-2-CV.ipynb
Visualization of training loss and test accuracy

Confusion Matrix with best model at epoch 95

EOC-2 (Target version variants)

		Train		Test
Class	Serial No.	Depression	No. Images	Depression	No. Images
BMP-2	9563	17	233	-	-
BRDM-2	E-71	17	298	-	-
BTR-70	c71	17	233	-	-
T-72	132	17	232	-	-
BMP-2	9566	-	-	15, 17	428
BMP-2	c21	-	-	15, 17	429
T-72	812	-	-	15, 17	426
T-72	A04	-	-	15, 17	573
T-72	A05	-	-	15, 17	573
T-72	A07	-	-	15, 17	573
T-72	A10	-	-	15, 17	567

Training Set (Depression: 17$\degree$)

# BMP2, BRDM2, BTR70, T72 are selected from SOC training data

Test Set (Depression: 15$\degree$)

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TRAIN/17_DEG
│    ├ BMP2/SN_9566/*.001 (232 images)
│    ├ BMP2/SN_C21/*.002 (233 images)
│    ├ T72/SN_812/*.016   (231 images)
│    └ ...
└ ...

MSTAR-PublicT72Variants-CD2/MSTAR_PUBLIC_T_72_VARIANTS_CD2
├ 17_DEG/COL2/SCENE1
│    ├ A04/*.017   (299 images)
│    ├ A05/*.018   (299 images)
│    ├ A07/*.019   (299 images)
│    ├ A10/*.020   (296 images)
├    └ ...
└ ...

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TEST/15_DEG
│    ├ BMP2/SN_9566/*.001 (196 images)
│    ├ BMP2/SN_C21/*.002 (196 images)
│    ├ T72/SN_812/*.0176  (195 images)
│    └ ...
└ ...

MSTAR-PublicT72Variants-CD1/MSTAR_PUBLIC_T_72_VARIANTS_CD1
├ 15_DEG/COL2/SCENE1
│    ├ A04/*.017   (274 images)
│    ├ A05/*.018   (274 images)
│    ├ A07/*.019   (274 images)
│    ├ A10/*.020   (271 images)
├    └ ...
└ ...

Quick Start Guide for Training

Dataset Preparation
- Download the dataset.zip
- After extracting it, you can find train and test directories inside raw directory.
- Place the two directories (train and test) to the dataset/eoc-2-vv/raw.

$ cd src/data 
$ python3 generate_dataset.py --is_train=True --use_phase=True --chip_size=100 --patch_size=94 --use_phase=True --dataset=eoc-2-vv 
$ python3 generate_dataset.py --is_train=False --use_phase=True --chip_size=128 --patch_size=128  --use_phase=True --dataset=eoc-2-vv
$ cd ..
$ python3 train.py --config_name=config/AConvNet-EOC-2-CV.json

Results of EOC-2 Version Variants

Final Accuracy is 97.17% at epoch 88 (The official accuracy is 98.60%)
You can see the details in notebook/experiments-EOC-2-VV.ipynb
Visualization of training loss and test accuracy

Confusion Matrix with best model at epoch 88

Outlier Rejection

		Train		Test		Remarks.
Class	Serial No.	Depression	No. Images	Depression	No. Images	Type.
BMP-2	9563	17	233	15	196	Known
BTR-70	c71	17	233	15	196	Known
T-72	132	17	232	15	196	Known
2S1	b01	17	-	15	274	Confuser
ZSU-234	d08	17	-	15	274	Confuser

Training Set (Known targets in Depression: 17$\degree$)

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TRAIN/17_DEG    # KNOWN
│    ├ BMP2/SN_9563/*.000 (233 images)
│    ├ BTR70/SN_C71/*.004 (233 images)
│    └ T72/SN_132/*.015   (232 images)
└ ...

MSTAR-PublicMixedTargets-CD2/MSTAR_PUBLIC_MIXED_TARGETS_CD2
├ 17_DEG                  # Confuser
│    └ COL2/SCENE1
│        ├ 2S1/*.000            (299 images)
│        └ ZIL131/*.025         (299 images)
└ ...

Test Set (Known targets and confuser targets in Depression: 15$\degree$)

MSTAR-PublicTargetChips-T72-BMP2-BTR70-SLICY/MSTAR_PUBLIC_TARGETS_CHIPS_T72_BMP2_BTR70_SLICY
├ TARGETS/TEST/15_DEG     # KNOWN
│    ├ BMP2/SN_9563/*.000 (195 images)
│    ├ BTR70/SN_C71/*.004 (196 images)
│    └ T72/SN_132/*.015   (196 images)
└ ...

MSTAR-PublicMixedTargets-CD1/MSTAR_PUBLIC_MIXED_TARGETS_CD1
├ 15_DEG                  # Confuser
│    └ COL2/SCENE1
│        ├ 2S1/*.000            (274 images)
│        └ ZIL131/*.025         (274 images)
└ ...

Quick Start Guide for Training

Dataset Preparation
- Download the dataset.zip
- After extracting it, you can find train and test directories inside raw directory.
- Place the two directories (train and test) to the dataset/confuser-rejection/raw.

$ cd src/data 
$ python3 generate_dataset.py --is_train=True --use_phase=True --chip_size=100 --patch_size=94 --use_phase=True --dataset=confuser-rejection 
$ python3 generate_dataset.py --is_train=False --use_phase=True --chip_size=128 --patch_size=128  --use_phase=True --dataset=confuser-rejection
$ cd ..
$ 
$ # Remove Confuser objects when training to check validation accuracy with known targets
$ cd ../dataset/confuser-rejection/test
$ rm -rf 2S1
$ rm -rf ZIL131
$ cd -
$
$ python3 train.py --config_name=config/AConvNet-CR.json
$
$ # Restore the unknown targets 
$ cd ../dataset/confuser-rejection/
$ rm -rf test
$ python3 generate_dataset.py --is_train=False --use_phase=True --chip_size=128 --patch_size=128  --use_phase=True --dataset=confuser-rejection

Results of Outlier Rejection

Final Accuracy for known targets is 98.81% at epoch 39
You can see the details in notebook/experiments-CR.ipynb
Visualization of training loss and test accuracy

Confusion Matrix with best model at epoch 88

Outlier Rejection: TODO: LOoking for more details..

# Rules
output_probability = model(image)
is_confuser = output_probability < thresh

if sum(is_confuser) == 3:
    target is confuser
else:
    target is known

Details about the specific environment of this repository


OS	Ubuntu 20.04 LTS
CPU	Intel i7-10700k
GPU	RTX 2080Ti 11GB
Memory	32GB
SSD	500GB
HDD	2TB

Citation

@ARTICLE{7460942,
  author={S. {Chen} and H. {Wang} and F. {Xu} and Y. {Jin}},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Target Classification Using the Deep Convolutional Networks for SAR Images}, 
  year={2016},
  volume={54},
  number={8},
  pages={4806-4817},
  doi={10.1109/TGRS.2016.2551720}
}

References

[1] G. Dong, N. Wang, and G. Kuang, "Sparse representation of monogenic signal: With application to target recognition in SAR images," IEEE Signal Process. Lett., vol. 21, no. 8, pp. 952-956, Aug. 2014.

TODO

zxs1652/AConvNet-pytorch

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