False Alarm Reduction in Wavelength–Resolution SAR Change Detection Schemes by Using a Convolutional Neural Network

Coherent multipolarization synthetic aperture radar (SAR) change detection exploiting data collected from $N$ multiple polarimetric channels is addressed in this letter. The problem is formulated as a binary hypothesis testing problem, and a special block-diagonal structure for the polarimetric covariance matrix is forced to design a detector based on the generalized likelihood ratio test (GLRT) criterion. It is shown that the structured decision rule ensures the constant false alarm rate property with respect to the unknown disturbance covariance. Results on both simulated and real high-resolution SAR data show the effectiveness of the considered decision rule and its superiority against the traditional unstructured GLRT in some scenarios of practical interest.

This paper presents a supervised polarimetric synthetic aperture radar (PolSAR) change detection method applied to specific land cover types. For each pixel of a PolSAR image, its target scattering vector can be modeled as having a complex multivariate normal distribution. Based on this assumption, the joint distribution of two corresponding vectors in a pair of PolSAR images is derived. Then, a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented. Subsequently, the Kittler and Illingworth minimum error threshold segmentation method is applied to extract the specific changed areas. Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou, China, demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images, especially the areas that have changed to water.

Abstract. In deep learning based synthetic aperture radar (SAR) change detection, selecting samples of high quality is a crucial step. In this work, we have proposed a refined sample selection algorithm for unsupervised SAR change detection. The propose and incorporation of volume control factors and multi-hierarchical fuzzy c-means (MH-FCM) algorithm generate samples of large diversity and high confidence, thus satisfying the needs for high quality samples. The method includes two phases: firstly, an enhanced difference image is constructed according to the difference consistency between single pixels and their neighbourhoods, and a triangular threshold segmentation method is then proposed to determine the volume control factors for sample selection. MH-FCM is developed to classify the log mean ratio difference image into 4 classes. Secondly, a dual-channel convolution neural network with an adaptive weighted loss is adopted to learn and predict the input and to obtain the change detection result. Experimental results of the Gaofen-3 dataset in Beijing have validated the effectiveness and usefulness of the proposed method.

With its all-day and all-weather imaging capability, Synthetic Aperture Radar (SAR) has shown great potential in crop identification. This letter puts forward a novel method of extracting winter wheat planting area by means of SAR change detection. Based on the phenological differences between winter wheat and other crops, the Difference Image (DI) is generated firstly by the different temporal SAR images. Then, Object Markov Random Field (OMRF) model is used to DI pre-classification. Finally, Convolutional Neural Network (CNN) is utilized to obtain the better area extraction result of winter wheat. The area extraction accuracy is proved by the field survey data, with an accuracy of 90.53%. This research can provide a new idea and method for agricultural remote sensing monitoring.

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.

Icebergs impose a significant threat to shipping, offshore oil operations, and underwater pipelines. Detecting and monitoring icebergs in the North Atlantic Ocean is particularly challenging due to frequent cloud cover. Synthetic Aperture Radar (SAR) has emerged as an effective solution for addressing these challenges. This study presents a novel method for detecting icebergs under varying sea conditions using C-band dual-polarimetric imagery from the RADARSAT Constellation Mission (RCM) using the data collected along Canada’s east coast during iceberg-prone seasons in 2022 and 2023. Our approach processes large SAR images by dividing them into 100 x 100-pixel patches, each covering an area of 5 km x 5 km, and identifies icebergs within each patch. The method combines statistical features, which highlight subtle patterns in RCM imagery, with high-dimensional features extracted from three pre-trained convolutional neural network (CNN) models. These features are further enhanced with climate parameters and classified using the LightGBM algorithm. To further reduce false alarms (FAs), a Constant False Alarm Rate (CFAR) postprocessing step was applied, enhancing the reliability of the detection process. The proposed approach demonstrates exceptional performance, achieving a 99.08% accuracy rate, a false alarm (FAs) rate of only 1%, and an AUC value nearing 1.

Recent growth in the tasking and collection of Synthetic Aperture Radar (SAR) imagery, in particular commercial satellite availability, provides new opportunities for wide area change monitoring. Classic applications of change detection in SAR compare individual pixels, but as higher resolution imagery has become widely available there is an opportunity to leverage structural image content to produce more informative change identification. Deep learning techniques encompass the state of the art in identifying structure in imagery, but are notoriously data hungry. A recent body of research has grown around a technique called self-supervised representation learning (SSRL) to help minimize the need for handmade labels. We build on this research and train a model for use in SAR change detection. We leverage a SSRL approach known as contrastive learning, which encourages a deep learning model to identify salient image features through noise and other augmentations without an immediate need for hand engineered labels. The representation learned through this process can then be applied to other supervised, or unsupervised tasks, and we demonstrate the use of this learned embedding to identify change across SAR image pairs.

The development of tools for automated analysis of data collected for mine countermeasures (MCM) search, classify and map (SCM) has predominantly focused on automated target recognition for individual mines. As MCM target detection becomes more automated, detecting spatial patterns in the positioning of mines should follow. Natural restrictions on mine deployment such as depth and bottom contour as well as mine deployment methodology and protocol effectively create mine line patterns that are typical to coastal areas. We have developed a mine line detection algorithm that uses nonparametric spatial analysis to identify mine-like contacts (MLCs) that fit into mine line patterns dictated by standard mine laying protocol in coastal regions. In general, a set of MLCs reported from a mine detection sensor will consist of both actual mines and false alarms; the algorithm classifies every MLC in this set into one of three classes: fixed — the MLCψfits into a mine line pattern with fixed mine spacing; random -the MLCψfits into a mine line pattern with random mine spacing; or false alarm-ψ-the MLCψdoes not fit into a mine line pattern and is subsequently not considered a mine. The latest version of the algorithm utilizes nonparametric techniques and requires no knowledge of either the detection sensor's probability of detection (P d ) or probability of false alarm (P fa ). A nonparametric anomaly detector first identifies regions with unusually high MLC density for more detailed analysis. Suspected regions are subsequently analyzed for unusually regular patterns of MLCs. The probabilities that the observed target densities and target patterns could occur simply by chance are estimated by comparison with results from nearby areas using basic information about mine deployment. To ensure that a mine line's full extent is captured, the algorithm attempts to grow a line after it is detected. And a more detailed probability analysis distinguishes random mine lines from fixed-spaced mine lines. Extensive Matlab simulations characterize the algorithm's performance across a wide range of operating environments and simulated minefield configurations. In total, ten replications each of 1,367 different configurations have been evaluated. These configurations include three different water depths (10 ft., 6 ft. and 200 ft.), six false notional alarm rates (1, 2,5,10,15 and 20 false alarms per sq. NM), three false alarm distributions (uniform, sloped, and striped), three sensor probabilities of detections (0.4, 0.6 and 0.9), three target confidence scores (0.6,0.8 and 1.0) and four false alarm confidence scores (0.5, 0.6, 0.8 and 1.0). The range of simulation parameters was selected in part as an attempt to push the algorithm past its acceptable performance limits. However, the algorithm performed remarkably well even under the worst conditions. For MLC sets with false alarm rates as high as 20 per square nautical mile, the algorithm correctly classifies over 98% of the MLCs as mines and detects 97% of the mine lines. Furthermore, it reduces the false alarm rate from 20 to 0.2 per square nautical mile with a false mine line rate of 0.14 false lines per square nautical mile. The algorithm achieves even lower false alarm rates when the initial false alarm rate is less than 20. The automated mine line detection discovers many obscure lines that are virtually undetectable to the naked eye.

The high rate of false arrhythmia alarms in Intensive Care Units (ICUs) can lead to disruption of care, negatively impacting patients’ health through noise disturbances, and slow staff response time due to alarm fatigue. Prior false-alarm reduction approaches are often rule-based and require hand-crafted features from physiological waveforms as inputs to machine learning classifiers. Despite considerable prior efforts to address the problem, false alarms are a continuing problem in the ICUs. In this work, we present a deep learning framework to automatically learn feature representations of physiological waveforms using convolutional neural networks (CNNs) to discriminate between true vs. false arrhythmia alarms. We use Contrastive Learning to simultaneously minimize a binary cross entropy classification loss and a proposed similarity loss from pair-wise comparisons of waveform segments over time as a discriminative constraint. Furthermore, we augment our deep models with learned embeddings from a rule-based method to leverage prior domain knowledge for each alarm type. We evaluate our method using the dataset from the 2015 PhysioNet Computing in Cardiology Challenge. Ablation analysis demonstrates that Contrastive Learning significantly improves the performance of a combined deep learning and rule-based-embedding approach. Our results indicate that the final proposed deep learning framework achieves superior performance in comparison to the winning entries of the Challenge.

ABSTRACTOil tank detection is a challenging task, primarily due to high time-consumption. This paper aims at further investigating this challenge and proposes a new hierarchical approach to detect oil tanks, especially with respect to how false alarm rates are reduced. The proposed approach is divided into four stages: region of interest (ROI) extraction, circular object detection, feature extraction, and classification. The first stage, which is a key component of this approach to reduce false alarm and processing time, is applied by an improved faster region-based convolutional neural network (Faster R-CNN) to extract oil depots. In the second stage, a number of candidate objects of the target are selected from the extracted ROIs by a fast circle detection method. Afterwards, in the third stage, a robust feature extractor based on a combination of the output feature vectors from convolutional neural network (CNN), as a high-level feature extractor, and histogram of oriented gradients (HOG), as a low-level feature extractor, are used for representing features of various targets. Finally, the support vector machine (SVM) is employed for classification. The experimental results confirm that the proposed approach has good prediction accuracy and is able to reduce the false alarm rates.

Development of an algorithm to discriminate between valid and false alarms in a loose-parts monitoring system

Timely information on lake ice cover conditions is critical in support of commercial shipping, winter-road transportation, and winter leisure activities such as snowmobiling and ice fishing. Monitoring of lake ice extent and ice phenology (i.e. dates associated with freeze-up/break-up and ice duration) is also valuable for improving numerical weather prediction (NWP) and for climate monitoring. The availability of free synthetic aperture radar (SAR) data from the European Space Agency’s Sentinel-1 A/B constellation provides an unprecedented opportunity to develop operational algorithms for lake ice cover mapping at a temporal frequency not available until now from SAR missions (ca. every 1–5 days depending on latitude). For NWP, rapid and accurate mapping of ice cover and open water areas in lake-rich regions is required. However, the classification of SAR imagery using traditional machine learning (ML) approaches is challenging due to the large data volume generated by imaging systems such as Sentinel-1 SAR as well as the complexity of radar signatures as a function of sensor and target characteristics. For lakes specifically, radar backscatter can vary greatly with incidence angle, polarization and surface properties (e.g. calm and wind-roughed open water, new thin ice, surface roughness due to the presence of pressure ridges, ice type, melting of ice and on-ice snow). To address the challenge of large-scale lake ice mapping, we investigate the GPU-boosted deep neural network approach that is efficient at handling big complex data. In this paper, we design a novel maximum a posterior (MAP) approach combining a convolutional neural network (CNN) and conditional random field (CRF) for better addressing the challenges of operational lake ice mapping. The proposed approach is tested on 17 Sentinel-1 dual-polarization (VV and VH) SAR images, where eight are used for training and validation and nine used for out-of-sample testing. To identify the optimal network architecture, an independent validation set is used to evaluate the performance of a series of six CNNs with increasing model complexity. The best model overcomes the noise effect in Sentinel-1 SAR imagery and the lake ice signature ambiguity issue; it achieves average classification accuracies of 97.10% and 97.14% for open water and ice, respectively, on the validation set. Moreover, the best model outperforms the last-ranked model by about 2% in terms of mean overall accuracy (OA), demonstrating the improvement and importance of model selection. The use of CRF can consistently improve the CNN by reducing the artefacts and noise effect in ice maps, outperforming the CNN model when used alone by about 3% in terms of mean OA on the validation set. The proposed CNN-CRF approach also achieves high accuracy on the nine test scenes, achieving a mean OA of 93.10%, demonstrating strong generalization capability that is important for SAR lake ice mapping.

Cyber-Physical Systems (CPS) tightly couple information technology with physical processes, which rises new vulnerabilities such as physical attacks that are beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This issue is even emphasized with the increasing autonomy in CPS. While this fact has motivated many defense mechanisms against sensor attacks, a clear vision on the timing and usability (or the false alarm rate) of attack detection still remains elusive. Existing works tend to pursue an unachievable goal of minimizing the detection delay and false alarm rate at the same time, while there is a clear trade-off between the two metrics. Instead, we argue that attack detection should bias different metrics when a system sits in different states. For example, if the system is close to unsafe states, reducing the detection delay is preferable to lowering the false alarm rate, and vice versa. To achieve this, we make the following contributions. In this paper, we propose a real-time adaptive sensor attack detection framework. The framework can dynamically adapt the detection delay and false alarm rate so as to meet a detection deadline and improve the usability according to different system status. The core component of this framework is an attack detector that identifies anomalies based on a CUSUM algorithm through monitoring the cumulative sum of difference (or residuals) between the nominal (predicted) and observed sensor values. We augment this algorithm with a drift parameter that can govern the detection delay and false alarm. The second component is a behavior predictor that estimates nominal sensor values fed to the core component for calculating the residuals. The predictor uses a deep learning model that is offline extracted from sensor data through leveraging convolutional neural network (CNN) and recurrent neural network (RNN). The model relies on little knowledge of the system (e.g., dynamics), but uncovers and exploits both the local and complex long-term dependencies in multivariate sequential sensor measurements. The third component is a drift adaptor that estimates a detection deadline and then determines the drift parameter fed to the detector component for adjusting the detection delay and false alarms. Finally, we implement the proposed framework and validate it using realistic sensor data of automotive CPS to demonstrate its efficiency and efficacy.

Detection of change in synthetic aperture radar images (SAR) has been considering an area of study in remote sensing and computer vision. It is important to classify the modified feature in the different SAR images obtained during various observing periods. Imaging of synthetic aperture radar (SAR) has been recognized as effective in the recognition of change than other imagery sensors, particularly when the earth is observed with cloudy, rainy, or hazy weather. The images taken by the synthetic aperture radar are low contrast and low brightness grayscale image. So, it is difficult to find the changes in SAR images. In this article, we proposed a detection of changes system where we use a convolution neural network (CNN) as a classification model, Modified Gauss-Log Ratio used to find the difference image of the two sample images. The classification result from CNN and the preclassification outcome is used to find out the final change map. The reliability and robustness of the proposed approach were shown by investigative findings using two SAR image data sets.KeywordsChange detectionSynthetic aperture radarSARCNNGauss-log ratio

The classification of objects and man-made structures on the ocean surface using synthetic aperture radar (SAR) imagery finds an important use in monitoring for icebergs and sea ice. Convolutional Neural Network (CNN) method can be employed to effectively classify objects imaged through SAR data. In this paper, the CNN performance is evaluated when three different preprocessing procedures are applied to the SAR image data to prepare the CNN's input data. Segmentation or normalization algorithms are implemented in the three procedures. Experimental results demonstrate an improvement over unsegmented and un-normalized images. Performance metrics for all three methods are approximately 94%, indicating that while some image preprocessing is required to achieve higher performance, the CNN tested is robust to the noise present in the SAR images used.