=Paper=
{{Paper
|id=Vol-2819/session2paper4
|storemode=property
|title=Convolutional Neural Networks for Feature Extraction and Automated Target Recognition in Synthetic Aperture Radar Images
|pdfUrl=https://ceur-ws.org/Vol-2819/session2paper4.pdf
|volume=Vol-2819
|authors=John Geldmacher,Christopher Yerkes,Ying Zhao
}}
==Convolutional Neural Networks for Feature Extraction and Automated Target Recognition in Synthetic Aperture Radar Images==
https://ceur-ws.org/Vol-2819/session2paper4.pdf
Convolutional Neural Networks for Feature
Extraction and Automated Target Recognition in
Synthetic Aperture Radar Images
John Geldmacher, Christopher Yerkes, and Ying Zhao, Memeber, IEEE
Abstract—Advances in the development of deep neural networks a synthetic aperture radar (SAR) sensor. Both IR and SAR
and other machine learning algorithms combined with ever more images require a trained imagery analyst to reliably identify
powerful hardware and the huge amount of data available on the targets. A repetitive and time consuming task that currently
internet has led to a revolution in ML research and applications.
These advances present massive potential and opportunity for military requires human expertise, importantly and creatively, is an
applications such as the analysis of Synthetic Aperture Radar (SAR) ideal problem for deep learning. Automated target recognition
imagery. SAR imagery is a useful tool capable of capturing high (ATR) seeks to reduce the total work load of analysts so that
resolution images regardless of cloud coverage and at night. However, their effort can be spent on the more human-centric tasks like
there is a limited amount of publicly available SAR data to train presenting and explaining intelligence to a decision maker.
a machine learning model. This paper shows how to successfully
dissect, modify, and re-architect cross-domain object recognition ATR is also intended to reduce the time from collection to
models such as the VGG-16 model, transfer learning models from exploitation by screening images at machine speeds rather than
the ImageNet, and the k-nearest neighbor (kNN) classifier. The paper manually. SAR ATR is complicated by the available data to
demonstrates that the combinations of these factors can significantly train and assess machine learning models. Unlike other image
and effectively improve the automated object recognition (ATR) classification tasks, there is not a large and freely available
of SAR clean and noisy images. The paper shows a potentially
inexpensive, accurate, transfer and unsurpervised learning SAR ATR amount of training data for researchers. Further, the data that
system when data labels are scarce and data are noisy, simplifying is publicly available only covers a small fraction of the types
the whole recognition for the tactical operation requirements in the of targets an effective SAR ATR system would be required to
area of SAR ATR. identify.
Keywords—k-nearest neighbor, kNN, deep learning, Synthetic
Aperture Radar images, SAR images, transfer learning, VGG-16
II. A DVANTAGES AND C HALLENGES OF S YNTHETIC
A PERTURE R ADAR (SAR) I MAGES , DATA D ESCRIPTION ,
I. I NTRODUCTION AND R ELATED W ORK
The analysis and classification of targets within imagery Synthetic Aperture Radar (SAR) is a radar mounted to a
captured by aerial and space-based systems provides the US moving platform that uses the platform’s motion to approx-
intelligence community and military geospatial intelligence imate the effect of a large antenna. The high resolution that
(GEOINT) personnel with important insights into adversary can be achieved by creating a radar with an effective aperture
force dispositions and intentions. It has also entered the much greater in size than is physically possible allows for
mainstream thanks to openly available tools like Google Earth. radar returns to be processed into images similar to what can
The high resolution of space-based sensors and common be achieved with a camera [19]. SAR imagery provides an im-
use of overhead imagery in everyday life means with the portant tool for the United States Intelligence Community and
exception of decoys and camouflage, an average person is military geospatial intelligence (GEOINT) personnel because
now reasonably capable of identifying objects in electro- of its all-weather, day/night collection capability. Additionally,
optical (EO) imagery. EO images are, however, limited by some wavelengths that SAR imaging systems operate in have
cloud coverage and daylight. About half of the time when a degree of foliage and ground penetrating capability allowing
a satellite in low earth orbit could image a target it will for the detection of buried objects or objects under tree cover
be night, necessitating the use of either an infrared (IR) or that would not be observable by other sensors such as EO
sensors.
This will certify that all author(s) of the above article/paper are employees These important advantages of SAR imaging for GEOINT
of the U.S. Government and performed this work as part of their employment,
and that the article/paper is therefore not subject to U.S. copyright protection. analysts do come with some significant drawbacks inherent to
No copyright. Use permitted under Creative Commons License Attribution SAR images. Because SAR images are not true optical images,
4.0 International (CC BY 4.0). In: Proceedings of AAAI Symposium on they are susceptible to noise generated by constructive and
the 2nd Workshop on Deep Models and Artificial Intelligence for Defense
Applications: Potentials, Theories, Practices, Tools, and Risks, November 11- destructive interference between radar reflections that appear
12, 2020, Virtual, published at http://ceur-ws.org/ as bright or dark spots called “speckle” in the image [19]. Also
J. Geldmacher, C. Yerkes, and Y. Zhao are with the Department of various materials and geometries will reflect the radar pulses
Information Sciences, Naval Postgraduate School, Monterey, CA, USA
C. Yerkes is also with Oettinger School of Science and Technology differently, creating blobs or blurs that can obscure the objects
Intelligence, National Intelligence University, Bethesda, MD, USA physical dimensions. These issues, as well as problems caused
images with the breakthrough feature extraction layers as
demonstrated in convolutional neural networks, produced
generally good results. An SVM method proposed by [27]
achieved 91% accuracy in a five-class test [27], while a
Bayesian classifier reported a 95.05% accuracy in a 10-classes
test [13].
In recent years, the work on classification of SAR imagery
has focused on the use of CNNs. In 2015, Morgan showed
that a relatively small CNN could achieve 92.1% accuracy
across the 10-class of the MSTAR dataset, roughly in line with
the shallow methods previously explored. Morgan’s method
also showed that a network trained on nine of the MSTAR
target classes could be retrained to include a tenth class 10-20
times faster than training a 10-class classifier from scratch.
The ability to more easily adapt the model to changes in
target sets represents an advantage over shallow classification
Fig. 1. Example Photographs and MSTAR Im- techniques [11]. This is especially valuable in a military
ages by Class. Photograph of BMP-2 from https : ATR context given the fluid nature of military operations,
//www.militaryf actory.com/armor/detail.asp?armor id = 50, where changes to the order of battle may necessitate updating
photograph of BTR-70 from https : //military.wikia.org/wiki/BT R −
70. All other photographs and SAR images adapted from the MSTAR a deployed ATR system. Malmgren-Hansen et al., explored
dataset. transfer learning from a CNN pre-trained on simulated SAR
images generated by using ray tracing software and detailed
computer aided design models of target systems. They showed
by Doppler shift in moving objects and radar shadows, make that model performance was improved, especially in cases
the identification and classification of objects in SAR images where the amount of training data was reduced [10]. The
a difficult and tedious task that also requires a well-trained technique of generating simulated SAR images for training
and experienced analyst. could also be valuable in a military SAR ATR context where
The the Moving and Stationary Target Acquisition and an insufficient amount of training data for some systems may
Recognition (MSTAR) data set is a publicly available data set exist.
consisting of synthetic aperture radar images of the following The use of a linear SVM as a replacement for the softmax
10 classes of military vehicles: activation that is typically used for multiclass classifiers in
1) 2S1: former Soviet Union (FSU) self-propelled artillery neural networks has been shown to be potentially more effec-
2) BMP-2: FSU infantry fighting vehicle tive for some classification tasks [22]. Transfer learning from
3) BRDM-2: FSU armored reconnaissance vehicle ImageNet to MSTAR with an SVM classifier was explored by
4) BTR-60: FSU armored personnel carrier [1] in 2018. Their methodology compared the performance of
5) BTR-70: FSU armored personnel carrier an SVM classifier trained on mid-level feature data extracted
6) D7: Caterpillar tractor frequently used in combat engi- from multiple layers from AlexNet, GoogLeNet, and VGG-
neering roles 16 neural networks without retraining the feature extracting
7) T-62: FSU main battle tank network. Although they reported over 99% accuracy when
8) T-72: FSU main battle tank classifying features extracted from mid-level convolutional
9) ZIL-131: FSU general purpose 6x6 truck layers from AlexNet, performance of the SVM on features
10) ZSU-23-4: FSU self-propelled anti-aircraft gun from fully-connected layers did not achieve 80% accuracy. The
11) SLICY: the Sandia Laboratories implementation of best performance reported on from the VGG-16 architecture
cylinders (SLICY). The SLICY consisting of simple was 92.3% from a mid-level convolutional layer, but only
geometric shapes such as cylinders, edge reflectors, and 49.2% and 46.3% from features extracted in the last two fully-
corner reflectors which could be used for calibration connected layers [1].
of sensors or for modeling the propagation of radar
reflections.
III. T RANSFER L EARNING AND F EATURE E XTRACTION
Fig. 1 shows the example photographs and MSTAR images
by class. It demonstrates the difficulties an imagery analyst CNNs require a very large amount of data to train an
would face when identifying targets in SAR imagery. The accurate model and it is not uncommon for data sets with
vehicles that are easily recognizable in photos become blurs in tens or even hundreds of thousands of images to be used
SAR images. Due to its public availability and ease of access when training a model. Transfer learning presents one possible
for researchers, the data set has become the standard for SAR solution when training a CNN on a limited data set by
image Automated Target Recognition (ATR) classification leveraging knowledge from a previously learned source task to
research. aid in learning a new target task [14]. In an image classification
ATR in SAR imagery using “shallow” classification meth- problem, transfer learning works by training a CNN on a data
ods, which are traditional classifiers applied directly to SAR set that has a very large number of images and freezing the
parameters for a certain number of layers and extracting mid-
level feature representations before training further layers and
the final classification layer [7].
ImageNet is an open source labeled image database orga-
nized in a branching hierarchical method of “synonym sets”
or “synsets”. For example, the “tank” synset is found in a
tree going from vehicle to wheeled vehicle to self-propelled
vehicle to armored vehicle to tank. The ImageNet database Fig. 2. The Original VGG-16 Architecture
consists of over 14 million labeled images organized into
over 21,000 synsets. Pre-trained ImageNets are often used in
transfer learning.
Transfer learning is typically used when source and target
tasks are not too dissimilar in order to avoid negative transfer.
Negative transfer occurs when the features learned in the
transfer learning method actually handicap the model perfor-
mance [14]. However, transfer learning becomes more useful
when a curious phenomenon that many deep neural networks
trained on natural images learn similar features across images
from different domains. Fig. 3. VGG-16 with Transfer learning by freezing the first six layers where
the weights taken directly from the ImageNet
Evidence shows that low and mid-level features could
represent basic ATR features in images such as texture,
corners, edges, and color blobs [9], and the low and mid-level two convolutional/pooling blocks frozen for training to take
neural network feature extraction function resembles the actual advantage of the broad feature detection of the pre-trained
biological and human neurons’ function. Low and mid-level network as shown in Fig. 3. Our method, as in Fig. 4, shows
of features extracted from CNNs are likely common across that the dense layer of 1024 features extracted are saved and
even dissimilar data sets. A transfer learning approach between used as the input to a shallow classifier.
different domains is feasible and ATR tasks are evidently In our experiment, the standard VGG-16 model is imple-
successful in cross-domain applications [2], [8]. For example, mented in the Keras application program interface (API) with
the application of transfer learning to remote sensing target TensorFlow as the backend. The ImageNet weights available
detection and classification was studied [16], which showed in the Keras are ported from the Visual Geometry Group at
that a CNN classifier trained on a photographic data set could Oxford University that developed the VGG architecture for
be retrained to perform remote sensing classification of ships ILSVRC-2014 localization and classification tasks [18]. We
at sea with a good performance. also use, Orange, which is an open source data science and
machine learning toolkit that allows users to easily manipulate
IV. SAR ATR: F EATURE E XTRACTION C OMBINED WITH data through a graphical user interface. Orange has several
S HALLOW C LASSIFIERS built-in machine learning algorithms and simplifies the data
A. Multistep Classifier management and pre-processing requirements to allow users
to experiment with approaches to machine learning and data
In practice and in cross-domain applications, very few
science [3].
people train an entire CNN from scratch because it is relatively
A CNN is trained on the 2200 training images with a 20%
rare to have a data set of sufficient size. For this reason, trans-
validation split. The training and test data were both then run
fer learning with feature extraction combined with shallow
through the retrained neural network. The last fully connected
classifiers are suitable choices for SAR images.
layer before the neural network’s output was saved as a 1024-
The network architecture employed in this paper was a
dimensional vector for each image as shown in Fig. 3.
modified VGG-16 architecture [18]. The original VGG-16 ar-
The extracted features run through the Orange workflow are
chitecture is shown in Fig. 2. It consists of two or three linked
pictured in Fig. 5. The precision and recall are used to compare
convolutional/pooling blocks, three fully-connected layers, and
the base CNN performance, kNN, SVM, and random forest
a softmax activation in the end to determine the class label.
The network employs a 3x3 kernel and a stride of one so
that each pixel is the center of a convolutional step. The
architecture has been modified to freeze model weights for
the first two convolutional/pooling blocks (e.g., the first six
layers). The model top has also been replaced with a fully-
connected layer, a dropout layer to mitigate overfitting, and
two final fully-connected layers with a softmax activation for
classification [16]. This is also referred as a modified VGG-
16 architecture or a VGG-16 architecture in this paper. It Fig. 4. The multistep classifier: Extract features from the VGG-16 and then
was initialized with the ImageNet weights and had the first apply a shallow classifier
Fig. 7. Examples of Noisy SAR images
Fig. 5. The Orange Workflow
Fig. 8. Comparison of VGG-16 with the multistep classifiers for added noise
Fig. 6. Comparison of VGG-16 with the multistep classifiers
classifiers. For the kNN classifier, k was set to 11 and weighted perturbation or adversarials’ deliberated manipulations [6],
Euclidian distance was used to determine which class label to [21]. To study the effect, random Gaussian noise with a noise
assign to test images. A sigmoid kernel was used in the SVM factor of 0.2 was added to the images from the data set.
classifier, and the random forest consisted of 10 decision trees. Fig. 7 shows an example of an original SAR image with one
These settings were unchanged in experiments two and three. added noise. The feature extraction from CNN and follow
on shallow classification process was then repeated without
retraining the base model in order to test the robustness of the
B. Results
model. The baseline model (i.e., modified VGG-16 model) was
The baseline model, which is the modified VGG-16, is then retrained on the noisy images for 30 epochs and accuracy
shown in Fig. 3. The modified VGG-16 without transfer was compared.
learning and trained exclusively on MSTAR, resulted in an Neither the neural network nor any of the multistep classi-
average precision and recall of 0.96. The same modified VGG- fiers proved robust enough to handle the addition of random
16 with full transfer learning of the convolutional layers with noise to the images. However, after retraining the kNN and
weights from ImageNet resulted in an average precision and SVM multistep classifiers perform better than the modified
recall of 0.88. Although, the transfer learning approach has VGG-16 with partial transfer learning.
the advantage of converging much more quickly than the CNN
initialized with random weights, the full transfer learning of
all convolutional weights and only retraining the CNN top V. D ISCUSSION
did not match the performance of the non-transfer learning Performance on the SLICY class is of interest because it
approach, suggesting some negative transfer occurs in the later demonstrates the model’s ability to discriminate an invalid
convolutional layers. As shown in Fig. 6, the best performed target from a valid target. All other classes, with the exception
is the modified VGG-16 with partial transfer learning in Fig. 3 of the D7, are former Soviet Union military equipment. The
and resulted an average precision and recall of 0.98. The D7 is a Caterpillar bulldozer. Up-armored versions of the D7
multistep classifier using a kNN classifier in Fig. 4 was able to and related equipment are often used in combat engineering
match the best baseline performance with an average precision roles. In a military context this means they are likely to be a
and call of 0.98, while the SVM and random forest classifiers valid target. As demonstrated by the high precision and recall
fell short of the baseline model’s performance. in this class across all models, valid targets are very rarely
classified as a SLICY (high precision) and the random objects
C. Adding Noise are not being accepted as valid targets (high recall).
As described before, ATR of SAR images are typically The performance of the kNN classifier is also notable since
sensitive to the noise in the images. A CNN is known to the use of an SVM for classification after feature extraction is
be vulnerable to the noisy models both from environmental previously studied; however, little research has been done on
ple) and no supervised learning or no class labels required
approach for SAR ATR. Recently, various learning-to-hash
algorithms [24] are used to approximate the exact nearest
neighbors, which translates a supervised learning problem and
kNN into an index/search problem [15], and simplifies the
whole recognition for the tactical operation requirements in
the area of SAR ATR. If there are no class labels of SAR
available, our multistep classifiers with transfer learning and
kNN can provide an unsupervised classification with a high
accuracy and confidence to match an object which looks like
another object seen before.
Fig. 10 also shows the comparison of kNN with other
supervised learning methods. The kNN method is the best
among all the methods for the average precision and recall,
where classification of the SLICY has a precision of 0.98 and
a recall of 1. The future work is to test on more and different
data sets (e.g., EO and IR data) to validate if the multistep
methods can apply to cross-domain ATR problems.
Fig. 9. VGG-16 TensorFlow architecture layout
VI. C ONCLUSION
Cross-domain transfer learning from photographs to SAR
imagery is effective for training a neural network both for
feature extraction and classification. A retrained neural net-
work can function as an efficient feature extractor for training
a shallow classifier. kNN and SVM classifiers are potentially
useful replacement for softmax activation in a neural network.
Multistep classification methods using a shallow classifier
trained on features extracted from a neural network, outper-
formed the base neural network when tested on noisy data
and as the amount of training data decreases. This is valuable
to improve CNN in a broader machine vision community by
applying feature extraction followed by shallow classifiers for
clean and noisy images. Transfer learning and kNN multistep
classification methods could be significant in terms of setting
up a robust image indexing system with minimum supervised
training and learning required.
Fig. 10. Multistep classifiers results: VGG-6 transfer learning features +
k-means (k=2048) + kNN & other methods in Orange
Currently, the analysis community does not have an estab-
lished standard for the percent of correctly identified targets
by an imagery analyst. Instead the analysis relies on the
user’s experience and confidence in their own work, providing
the performance of kNN rather than a softmax activation for
responses such as “possible main battle tank” or “likely BMP-
neural network output.
2”, and thus a direct comparison to expert-level performance
To further explore the relations of feature extraction, transfer is difficult to establish. Both the baseline model employing
learning, and kNN, we ran an additional experiment where transfer learning and the shallow classifiers using a neural
we first extracted the transfer weights of the first six layers network as a feature extractor performed with a high degree
of the VGG-16 architecture from ImageNet. Since the flatten of accuracy and would be valuable in an operational context
dimension is 32x32x128=131,072, as shown in Fig. 9, we as an aid to GEOINT analysts.
applied the unsupervised learning k-means algorithm to group
the 131,072 dimension into 2048 clusters. The reasoning
here is that the first six layers probably embed the best
features (texture, corners, edges, and color blobs) that can
be used for classification. Finally, We performed kNN and
other supervised learning methods in Orange based on the
2048 dimensional train and test data. Fig. 10 shows the test
data results from Orange for the VGG6-transfer-kmeans-kNN
method with an average precision and recall of 0.93. The six
layers of transfer learning together with k-means and kNN
provide an inexpensive (without GPU or AWS, for exam-
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