L-Band SAR Disaster Monitoring for Harbor Facilities Using Interferometric Analysis

Synthetic aperture radar (SAR) has become a major tool for disaster monitoring. Its all-weather capability enables us to monitor the affected area soon after the event happens. Since the first launch of spaceborne SAR, its amplitude images have been widely used for disaster observations. Nowadays, an accurate orbit control and scheduled frequent observations enable us to perform interferometric analysis of SAR (InSAR) and the use of interferometric coherence. Especially for L-band SAR, its long-lasting temporal coherence is an advantage to perform precise interferometric coherence analysis. In addition, recent high resolution SAR images are found to be useful for observing relatively small targets, e.g., individual buildings and facilities. In this chapter, we present basic theory of SAR observation, interferometric coherence analysis for the disaster monitoring, and its examples for the harbor facilities. In the actual case, DInSAR measurement could measure the subsidence of the quay wall with 3 cm error

合成開口レーダー干渉画像における歪みとその解消に関する研究

学位の種別:課程博士University of Tokyo(東京大学

Sensitivity and Limitation in Damage Detection for Individual Buildings Using InSAR Coherence—A Case Study in 2016 Kumamoto Earthquakes

In this paper, evaluation results are presented for multi-temporal interferometric coherence analysis using a Synthetic Aperture Radar (SAR) for damage assessment in an urban area. The latest space-borne SARs potentially have a high enough spatial resolution to assess individual buildings. However, interferometric coherence analysis has not been evaluated for its limitation in sensitivity and size of damaged buildings. In particular, the correlation between the coherence analysis and the damage level referred to by architectural assessments has been an open question. In this paper, analytical results using ALOS-2 PALSAR-2 datasets are presented from the 2016 Kumamoto earthquakes in Japan. For reference, building damage was assessed throughout the central urban area and specifically at a catastrophically damaged district. The results show that the buildings should be larger than a window size of the coherence for damage detection, and the damage level should be larger than Level-2 of 5, classified with the European Macroseismic Scale 1998 (EMS-98)

Time Variant and Narrow Bandwidth RFI Detection in Multi-Receiver SAR: Preliminary Study in ALOS-2

Latest SAR platforms carry multiple receivers in order to increase their swath with discrete phase center (DPC) approach or digital beam forming (DBF), and / or perform along track interferometry (ATI). The main purpose of placing multiple receivers is to distinguish slight differences of the scattered echo in those receivers so that we can extract the targets’ information further. From the point of view of RFI detection, those receivers can be used for advanced detection methods. As RFI is a direct wave, the received RFI signals are the same and only receiving time can shift between the receivers depending on incidence angle

Neural Network Fusion Processing and Inverse Mapping to Combine Multisensor Satellite Data and Analyze the Prominent Features

In the last decade, the increase of active and passive earth observation satellites has provided us with more remote sensing data. This fact has led to enhanced interest in the fusion of different satellite data since some of the satellites have properties complementary to others. Fusion techniques can improve the estimation in areas of interest by using the complementary information and inferring unknown parameters. They also have the potential to provide high-resolution detailed classification maps. Thus, we propose a neural network, which combines and analyzes the data obtained from synthetic aperture radar (SAR) and optical sensors to provide high-resolution classification maps. The neural network employs a novel activation function to construct a neural network explainability method termed as inverse mapping for prominent feature analysis. By applying inverse mapping to the data fusion neural network, we can understand which input features are the prominent contributors for which classification outputs. Inverse mapping realizes backward signal flow based on teacher-signal backpropagation dynamics, which is consistent with its forward processing. It performs the contribution analysis of the data pixel by pixel and class by class. In this article, we focus on earthquake damage detection by dealing with SAR and optical sensor data of the 2018 Sulawesi earthquake in Indonesia. The fusion-based results show increased classification accuracy compared to the results of independent sensors. Moreover, we observe that inverse mapping shows reasonable explanations in a consistent manner. It also indicates the contributions of features different from straightforward counterparts, namely, pre- and post-seismic features, in the detection of particular classes

Full-Learning Rotational Quaternion Convolutional Neural Networks and Confluence of Differently Represented Data for PolSAR Land Classification

Quaternion convolutional neural networks (QCNNs) expand the range of their applications in processing optical and polarimetric synthetic aperture radar (PolSAR) images. Conventional real-valued convolutional neural networks (RVCNNs) compress a three-channel input image into a single-channel feature map and ignore the relationship among the channels. In contrast, QCNNs deal with the input image as a single quaternion matrix and perform quaternion operation without the reduction of the channels. They can learn the interrelationship among the channel components. Though there exist two types of QCNNs, they have problems, respectively. One type conducts physically unclear quaternion convolution by using simple quaternionic multiplications. The other employs quaternion rotations with fixed axes, resulting in impairment of expression ability. In this article, we propose full-learning rotational QCNNs, which perform quaternion rotation in convolution, and update all the four parameters of a quaternion weight by backpropagation. They realize quaternion rotational convolution with high expression ability. We also propose using two different kinds of features, namely PolSAR pseudocolor features and Stokes vectors normalized by their total power. These two features allow neural networks to learn totally different characteristics of land surface. We train two networks with these features independently. Then, we merge their two classification results to obtain final decision to compensate for the shortcomings of the respective features. Experiments demonstrate that our proposed QCNNs show better classification performance than that of RVCNNs and the two existing QCNNs. We also find that the combination of the two features improves final classification results measured by F-scores