This paper describes a new measurement campaign for SAR images. The data consists of images collected by the Swedish LORA system associated with VHF-band (19-90 MHz). Due to the system frequency, detecting targets concealed in a forest is possible. Thus, this paper aims to share with the community the results of utilizing new VHF-band SAR data that allows the development of new methods for target and other change detection. In particular, to show the applicability of the new data set, a simple change detection method was performed to detect targets in a forest, resulting in 100% of detection, associated with no false alarm in a particular region of interest.
Feature extraction techniques play an essential role in classifying and recognizing targets in synthetic aperture radar (SAR) images. This article proposes a hybrid feature extraction technique based on convolutional neural networks and principal component analysis. The proposed method is used to extract features of oil rigs and ships in C-band synthetic aperture radar polarimetric images obtained with the Sentinel-1 satellite system. The extracted features are used as input in the logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision tree (DT), and k-nearest-neighbors (kNN) classification algorithms. Furthermore, the statistical tests of Kruskal-Wallis and Dunn were considered to show that the proposed extraction algorithm has a significant impact on the performance of the classifiers.
This article investigates basic preprocessing techniques to improve classification accuracy in the context of Automatic Target Recognition (ATR) of non-cooperative targets in Synthetic Aperture Radar (SAR) images. Preprocessing techniques are considered in synthetic data providing different inputs to a model-based classification algorithm. Experiments with preprocessing techniques such as area reduction, morphological transformations, and speckle filtering were run using ten target classes of the SAMPLE dataset. The classification is performed in measure data using scattering centers as features. The results reveal that the original image without any preprocessing techniques reached the best classification performance. However, investigations with other classifiers that use different features may benefit from such preprocessing techniques.
This paper presents the proposal of a new change detection method for intensity VHF wavelength-resolution images. High-amplitude pixels are related to the presence of strong scatterers, resulting in high detection probability performance. However, the number of false alarms tends to be high too. In this initial study, difference images are considered to reduce the influence of the strong scatterers that are not related to targets, i.e., present in both surveillance and reference images. The proposed change detection method is based on a likelihood-ratio test, where the tested hypothesis is the bivariate exponential distribution. The derivation of the proposed likelihood test is presented. Finally, the proposed change detection method is assessed considering data measured with the CARABAS II VHF UWB SAR system. Preliminary results show that the proposed method is efficient in detecting positive changes.
Change detection methods are frequently associated with wavelength-resolution synthetic aperture radar (SAR) images for foliage-penetrating (FOPEN) applications (e.g., the detection of concealed targets in forestry areas), being a research topic of interest over the last decades. The challenge associated with the design of automated change detection techniques goes beyond performing the target detection. It is also related to clutter suppression aiming at a low false alarm rate (FAR). The problem of detecting targets and removing content in SAR data can be treated as an unsupervised signal separation problem, usually referred to as blind source separation (BSS). Additionally, low frequency wavelength-resolution SAR images can be considered to follow an additive separation model due to their backscatter characteristics. In this context, it is possible to explore robust principal component analysis (RPCA) as a source-separation method for problems in which the mixing model is additive and two-dimensional, as the interest SAR images. This paper presents a change detection method for wavelengthresolution SAR images based on the RPCA via principal component pursuit (PCP), considering the use of small image stacks to explore the data diversity from measurements of different flight headings. The proposed method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high frequency (VHF) SAR system CARABAS II. The experimental results show that the proposed method can achieve a high probability of detection (PD) values for a low FAR (i.e., PD of 0.98 for a FAR of 0.41 objects per square kilometer). Finally, discussions regarding the use of the RPCA in change detection methods and the diversity gains are provided in the paper.
Global Navigation Satellite System radio occultation (GNSS-RO) is an important technique used to sound the Earth's atmosphere and provide data products to numerical weather prediction (NWP) systems as well as to climate research. It provides a high vertical resolution and SI-traceability that are both valuable complements to other Earth observation systems. In addition to direct components refracted in the atmosphere, many received RO signals contain reflected components thanks to the specular and relatively smooth characteristics of the ocean. These reflected components can interfere the retrieval of the direct part of the signal, and can also contain meteorological information of their own, e.g., information about the refractivity at the Earth's surface. While the conventional method to detect such reflections is by using radio-holographic methods, it has been shown that it is possible to see reflections using wave optics inversion, specifically while inspecting the amplitude of the output of phase matching (PM). The primary objective of this paper is to analyze the appearance of these reflections in the amplitude output from another wave optics algorithm, namely the much faster full spectrum inversion (FSI). PM and FSI are closely related algorithms - they both use the method of stationary phase to derive the bending angle from a measured signal. We apply our own implementation of FSI to the same GNSS-RO measurements that PM was previously applied to and show that the amplitudes of the outputs again indicate reflection in the surface of the ocean. Our results show that the amplitudes output from the FSI and PM algorithms are practically identical and that the reflection signatures thus appear equally well.
Change detection is an important synthetic aperture radar (SAR) application, usually used to detect changes on the ground scene measurements in different moments in time. Traditionally, change detection algorithm (CDA) is mainly designed for two synthetic aperture radar (SAR) images retrieved at different instants. However, more images can be used to improve the algorithms performance, witch emerges as a research topic on SAR change detection. Image stack information can be treated as a data series over time and can be modeled by autoregressive (AR) models. Thus, we present some initial findings on SAR change detection based on image stack considering AR models. Applying AR model for each pixel position in the image stack, we obtained an estimated image of the ground scene which can be used as a reference image for CDA. The experimental results reveal that ground scene estimates by the AR models is accurate and can be used for change detection applications.
The paper represents investigations on SAR image statistics and adaptive signal processing for change detection. The investigations show that the amplitude distributions of SAR images with possibly detected changes, that is retrieved with a linear subtraction operator, can approximately be represented by the probability density function of the Gaussian or normal distribution. This allows emerging the idea to use the available adaptive signal processing techniques for change detection. The experiments indicate the promising change detection results obtained with an adaptive line enhancer, one of the adaptive signal processing technique. The experiments are conducted on the data collected by CARABAS, a UWB low frequency SAR system.
The paper presents another possibility to focus moving targets using normalized relative speed (NRS). Similar to the currently used focusing approach, the focusing approach proposed in this paper aims at the ultrawideband and ultrawidebeam synthetic aperture radar systems (UWB SAR) like CARABAS-II. The proposal is shown to overcome the shortcomings of the original focusing approach and can be extended to more complicated cases, for example bistatic SAR.
The paper presents a study of the capability of time- and frequency-domain algorithms for bistatic SAR processing. Two typical algorithms, Bistatic Fast Backprojection (BiFBP) and Bistatic Range Doppler (BiRDA), which are both available for general bistatic geometry, are selected as the examples of time- and frequency-domain algorithms in this study. Their capability is evaluated based on some criteria such as processing time required by the algorithms to reconstruct SAR images from bistatic SAR data and the quality assessments of those SAR images.
Analyses in this study show that measurements under currently used definitions on SAR image quality measurement
may be unsuitable for UWB SAR. The main objective of this paper is therefore to propose a definition based on the
shape of a single point target in a SAR image which is more suitable for UWB SAR. We use both real and simulated
data based on the airborne UWB low frequency SAR CARABAS-II in experiments. The time-domain algorithm Global
Backprojection (GBP) is selected for the image formation in this study.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.