Change Detection with SAR Imagery
The basic algorithm for Synthetic Aperture Radar (SAR) Image formation assumes that all motion between the imaged scene and the radar platform during the image collection is due to platform motion. Any moving objects in the scene will be distorted, blurred, or offset. If a scene is imaged successively with some time interval, and there is a change or movement between the images, it is frequently desired to detect significant changes between the images. SAR images are different from conventional optical images, and are similar to holograms, as phase information is retained, not just intensity. They also have speckle noise, similar to holographic images.
This report investigates techniques for detecting fine scale scene changes
using repeat pass Synthetic Aperture Radar (SAR) imagery. As SAR is a
coherent imaging system two forms of change detection may be considered,
namely incoherent and coherent change detection.
- Incoherent change detection (ICD) identifies changes in the mean backscatter power of a scene typically via
an average intensity ratio change statistic.
- Coherent change detection (CCD) on the
other hand, identifies changes in both the amplitude and phase of the transduced imagery using the sample coherence change statistic.
Coherent change
detection has the potential to detect very subtle scene changes to the
sub-resolution cell scattering structure that may be undetectable using incoherent techniques. The repeat pass SAR imagery however, must be acquired
and processed interferometrically. Obtaining good CCD processing places greater constraints on replication of the direction of radar platform motion and imaged scene line of sight range and direction than ICD processing which may preclude its use for Satellite SAR, and constrain mission planning for aircraft platforms. CCD processing requires significantly larger image data sets than ICD, as both magnitude and phase of each pixel must be retained.
ICD Change Detection Using Magnitude Only IMAGES BASED ON IMAGE FUSION
▶ JAVA IEEE Projects 2012 CHANGE DETECTION IN SYNTHETIC APERTURE RADAR IMAGES BASED ON IMAGE FUSION - YouTube
Published on Feb 5, 2013
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Maoguo Gong; Zhiqiang Zhou; Jingjing Ma, "Change Detection in Synthetic
Aperture Radar Images based on Image Fusion and Fuzzy Clustering,"
Image Processing, IEEE Transactions on , vol.21, no.4, pp.2141,2151, April 2012
doi: 10.1109/TIP.2011.2170702
Abstract:
This paper presents an unsupervised distribution-free change detection
approach for synthetic aperture radar (SAR) images based on an image
fusion strategy and a novel fuzzy clustering algorithm. The image fusion
technique is introduced to generate a difference image by using
complementary information from a mean-ratio image and a log-ratio image.
In order to restrain the background information and enhance the
information of changed regions in the fused difference image, wavelet
fusion rules based on an average operator and minimum local area energy
are chosen to fuse the wavelet coefficients for a low-frequency band and
a high-frequency band, respectively. A reformulated fuzzy
local-information C-means clustering algorithm is proposed for
classifying changed and unchanged regions in the fused difference image.
It incorporates the information about spatial context in a novel fuzzy
way for the purpose of enhancing the changed information and of reducing
the effect of speckle noise. Experiments on real SAR images show that
the image fusion strategy integrates the advantages of the log-ratio
operator and the mean-ratio operator and gains a better performance. The
change detection results obtained by the improved fuzzy clustering
algorithm exhibited lower error than its preexistences.
keywords:
{radar imaging;synthetic aperture radar;complementary information;fuzzy
clustering;image fusion;mean ratio image;synthetic aperture radar
images;unsupervised distribution free change detection;Change detection
algorithms;Clustering algorithms;Damping;Discrete wavelet
transforms;Image fusion;Noise;Wavelet coefficients;Clustering;fuzzy
C-means (FCM) algorithm;image change detection;image fusion;synthetic
aperture radar (SAR);Algorithms;Fuzzy Logic;Image Enhancement;Image
Interpretation, Computer-Assisted;Imaging, Three-Dimensional;Pattern
Recognition, Automated;Radar;Reproducibility of Results;Sensitivity and
Specificity;Subtraction Technique},
Synthetic Aperture Radar Image Change Detection Using Fuzzy C-Means Clustering Algorithm
Abstract : This paper presents a novel approach to change
detection in synthetic aperture radar (SAR) images based on image fusion and
fuzzy clustering. The proposed approach use mean - ratio image and log - ratio
image to generate a difference image by image fusion technique. In order to
enhance the information of changed regions and background information in the
difference image i s based on the wavelet fusion rule. A reformulated fuzzy
local c means clustering algorithm is used for differentiating changed and unchanged
regions in the fused image, which is insensitive to noise and reduce the effect
of speckle noise. By this method we get a better performance and lower error
than the pre - existence.
Keywords: Image fusion, clustering , fuzzy c - means
algorithm (FCM), Synthetic Aperture Radar (SAR), image change detection
CCD Change Detection Using Complex IMAGES
CCD has been used extensively by Sandia in their Lynx and Copperhead SAR designs for UAV platforms. DSTO in Australia has also conducted experiments using their Ingara SAR.
spendergast: Sandia Copperhead Mini-SAR IED Detector proven in JIEDDO tests to Army
conference_spie99_paper4.PDF - spie_lynx.pdf
Sandia National Laboratories: Synthetic Aperture Radar (SAR) Imagery
This report examines the processing steps
required to form a coherent image pair and describes an interferometric
spotlight SAR processor for processing repeat pass collections acquired
with DSTO
Ingara X-band SAR. The detection performance of the commonly used
average
intensity ratio and sample coherence change statistics are provided as
well as
the performance of a recently proposed log likelihood change statistic.
The
three change statistics are applied to experimental repeat pass SAR data
to
demonstrate the relative performance of the change statistics.
Techniques for detecting fine scale scene changes using repeat pass spotlight Synthetic Aperture Radar (SAR) imagery are examined. Change detection is an
application to which SAR is particularly well suited since SARs can consistently produce
high quality fine resolution imagery from multiple repeat pass collections. Furthermore
the precise flight track measurements necessary for synthetic aperture formation allows
imagery to be acquired with good radiometric and geometric calibration as well as good
geolocation accuracy.
As SAR is a coherent imaging system two forms of change detection may be considered,
namely incoherent and coherent change detection. Incoherent change detection identifies
changes in the mean backscatter power of a scene. Typically the average image intensity
ratio of the image pair is computed to detect such changes. Coherent change detection
on the other hand, identifies changes in both the amplitude and phase of the transduced
imagery that arise in the interval between collections. The sample coherence of the image
pair is commonly used to quantify such changes. As the SAR image amplitude and phase
are sensitive to changes in the spatial distribution of scatterers within a resolution cell,
coherent change detection has the potential to detect very subtle scene changes that may
remained undetected using incoherent techniques. In order to realise the full potential of
coherent change detection however, SAR imagery must be acquired and processed interferometrically.
In particular the image pair must be acquired with careful control of the
repeat pass imaging geometries. Furthermore additional processing steps are required to
model, estimate and compensate for any mismatch between the SAR acquisition functions
and image formation processors employed to form the primary and repeat image pair.
This report describes the processing steps required to form a coherent image pair
suitable for interferometric processing. In particular imaging collection constraints are
discussed and the various sources of image decorrelation present in a repeat pass image
pair are described and quantified. A practical interferometric SAR processor for processing
repeat pass collections obtained from the DSTO Ingara X-band SAR is described.
Results
from a change detection experiment conducted with Ingara are given in which changes,
possibly due to the movement of sheep, are presented.
The theoretical detection performance of the incoherent average image intensity ratio
and the sample coherence are quantified in terms of receiver operator curves (ROC) i.e.,
the probability of detection plotted against probability of false alarm. A third recently
proposed coherent log likelihood change statistic is described and its theoretical detection
performance is shown to be superior to the commonly used average image intensity ratio
and the sample coherence.
The three change statistics are applied to two different experimental repeat pass SAR
collections each with controlled scene changes created using a rotary hoe and lawn mower.
In the first collection the repeat pass delay is 24 hours and for a false alarm rate of 1 in 20
the probability of detecting the rotary hoe changes is 0.23 in the sample coherence image
and 0.71 in the log likelihood ratio image. The changes are also detected in the averaged
image intensity ratio image with a probability of detection of 0.42
The second collection
was acquired over a different scene with a repeat pass delay of 2 hours. In this experiment
the rotary hoe changes are only detected in the sample coherence and log likelihood ratio
change images. For a false alarm rate of 1 in 55 the probability of detection in the sample
coherence image is 0.3 and in the log likelihood change image it is 0.68.
Theoretical and
simulated ROC plots for the two experimental cases show that for a fixed probability of
detection of 0.7 the log likelihood change statistic has approximately an order of magnitude
lower false alarm rate than the sample coherence. The improved detection performance of
the log likelihood change statistic is a step towards robust computer assisted exploitation
of coherent change detection data.
Additional Papers on SAR CCD processing
| Article Title | Multi-path SAR change detection |
| Publication Title | Radar Conference (RADAR), 2012 IEEE |
| ISBN | 978-1-4673-0656-0 |
| Posted Online Date | 7 Jun 2012 |
| Authors | Hu, Z.; Bryant, M.; Qiu, R.C. |
| Article Title | Activity detection in SAR CCD |
| Publication Title | Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International |
| ISBN | 978-1-4799-1114-1 |
| Posted Online Date | 27 Jan 2014 |
| Authors | Phillips, R.D. |
| Article Title | A generalized likelihood ratio test for SAR CCD |
| Publication Title | Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on |
| ISBN | 978-1-4673-5050-1 |
| Posted Online Date | 28 Mar 2013 |
| Authors | Newey, M.; Benitz, G.; Kogon, S. |
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