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Register a new userChange Detection Using Synthetic Aperture Radar Videos
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Added by
Hasara Maithree
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Many researches have been carried out for change detection using temporal SAR images. In this paper an algorithm for change detection using SAR videos has been proposed. There are various challenges related to SAR videos such as high level of speckle noise, rotation of SAR image frames of the video around a particular axis due to the circular movement of airborne vehicle, non-uniform back scattering of SAR pulses. Hence conventional change detection algorithms used for optical videos and SAR temporal images cannot be directly utilized for SAR videos. We propose an algorithm which is a combination of optical flow calculation using Lucas Kanade (LK) method and blob detection. The developed method follows a four steps approach: image filtering and enhancement, applying LK method, blob analysis and combining LK method with blob analysis. The performance of the developed approach was tested on SAR videos available on Sandia National Laboratories website and SAR videos generated by a SAR simulator.
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Change detection from synthetic aperture radar (SAR) imagery is a critical yet challenging task. Existing methods mainly focus on feature extraction in spatial domain, and little attention has been paid to frequency domain. Furthermore, in patch-wise feature analysis, some noisy features in the marginal region may be introduced. To tackle the above two challenges, we propose a Dual-Domain Network. Specifically, we take features from the discrete cosine transform domain into consideration and the reshaped DCT coefficients are integrated into the proposed model as the frequency domain branch. Feature representations from both frequency and spatial domain are exploited to alleviate the speckle noise. In addition, we further propose a multi-region convolution module, which emphasizes the central region of each patch. The contextual information and central region features are modeled adaptively. The experimental results on three SAR datasets demonstrate the effectiveness of the proposed model. Our codes are available at https://github.com/summitgao/SAR_CD_DDNet.
Although deep learning has achieved great success in image classification tasks, its performance is subject to the quantity and quality of training samples. For classification of polarimetric synthetic aperture radar (PolSAR) images, it is nearly impossible to annotate the images from visual interpretation. Therefore, it is urgent for remote sensing scientists to develop new techniques for PolSAR image classification under the condition of very few training samples. In this letter, we take the advantage of active learning and propose active ensemble deep learning (AEDL) for PolSAR image classification. We first show that only 35% of the predicted labels of a deep learning models snapshots near its convergence were exactly the same. The disagreement between snapshots is non-negligible. From the perspective of multiview learning, the snapshots together serve as a good committee to evaluate the importance of unlabeled instances. Using the snapshots committee to give out the informativeness of unlabeled data, the proposed AEDL achieved better performance on two real PolSAR images compared with standard active learning strategies. It achieved the same classification accuracy with only 86% and 55% of the training samples compared with breaking ties active learning and random selection for the Flevoland dataset.
Groundwater depletion impacts the sustainability of numerous groundwater-dependent vegetation (GDV) globally, placing significant stress on their capacity to provide environmental and ecological support for flora, fauna, and anthropic benefits. Industries such as mining, agriculture, and plantations are heavily reliant on groundwater, the over-exploitation of which risks impacting groundwater regimes, quality, and accessibility for nearby GDVs. Cost effective methods of GDV identification will enable strategic protection of these critical ecological systems, through improved and sustainable groundwater management by communities and industry. Recent application of synthetic aperture radar (SAR) earth observation data in Australia has demonstrated the utility of radar for identifying terrestrial groundwater-dependent ecosystems at scale. We propose a robust classification method to advance identification of GDVs at scale using processed SAR data products adapted from a recent previous method. The method includes the development of SARGDV, a binary classification model, which uses the extreme gradient boosting (XGBoost) algorithm in conjunction with three data cubes composed of Sentinel-1 SAR interferometric wide images. The images were collected as a one-year time series over Mount Gambier, a region in South Australia, known to support GDVs. The SARGDV model demonstrated high performance for classifying GDVs with 77% precision, 76% true positive rate and 96% accuracy. This method may be used to support the protection of GDV communities globally by providing a long term, cost-effective solution to identify GDVs over variable regions and climates, via the use of freely available, high-resolution, globally available Sentinel-1 SAR data sets.
We propose a saliency-based, multi-target detection and segmentation framework for multi-aspect, semi-coherent imagery formed from circular-scan, synthetic-aperture sonar (CSAS). Our framework relies on a multi-branch, convolutional encoder-decoder network (MB-CEDN). The encoder portion extracts features from one or more CSAS images of the targets. These features are then split off and fed into multiple decoders that perform pixel-level classification on the extracted features to roughly mask the target in an unsupervised-trained manner and detect foreground and background pixels in a supervised-trained manner. Each of these target-detection estimates provide different perspectives as to what constitute a target. These opinions are cascaded into a deep-parsing network to model contextual and spatial constraints that help isolate targets better than either solution estimate alone. We evaluate our framework using real-world CSAS data with five broad target classes. Since we are the first to consider both CSAS target detection and segmentation, we adapt existing image and video-processing network topologies from the literature for comparative purposes. We show that our framework outperforms supervised deep networks. It greatly outperforms state-of-the-art unsupervised approaches for diverse target and seafloor types.
Data and data sources have become increasingly essential in recent decades. Scientists and researchers require more data to deploy AI approaches as the field continues to improve. In recent years, the rapid technological advancements have had a significant impact on human existence. One major field for collecting data is satellite technology. With the fast development of various satellite sensor equipment, synthetic aperture radar (SAR) images have become an important source of data for a variety of research subjects, including environmental studies, urban studies, coastal extraction, water sources, etc. Change detection and coastline detection are both achieved using SAR pictures. However, speckle noise is a major problem in SAR imaging. Several solutions have been offered to address this issue. One solution is to expose SAR images to spatial fuzzy clustering. Another solution is to separate speech. This study utilises the spatial function to overcome speckle noise and cluster the SAR images with the highest achieved accuracy. The spatial function is proposed in this work since the likelihood of data falling into one cluster is what this function is all about. When the spatial function is employed to cluster data in fuzzy logic, the clustering outcomes improve. The proposed clustering technique is us
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