Enhancing coherence images for coherent change detection: An example on vehicle tracks in airborne sar images

In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper

Incorporating interferometric coherence into lulc classification of airborne polsar-images using fully convolutional networks

Inspired by the application of state-of-the-art Fully Convolutional Networks (FCNs) for the semantic segmentation of high-resolution optical imagery, recent works transfer this methodology successfully to pixel-wise land use and land cover (LULC) classification of PolSAR data. So far, mainly single PolSAR images are included in the FCN-based classification processes. To further increase classification accuracy, this paper presents an approach for integrating interferometric coherence derived from co-registered image pairs into a FCN-based classification framework. A network based on an encoder-decoder structure with two separated encoder branches is presented for this task. It extracts features from polarimetric backscattering intensities on the one hand and interferometric coherence on the other hand. Based on a joint representation of the complementary features pixel-wise classification is performed. To overcome the scarcity of labelled SAR data for training and testing, annotations are generated automatically by fusing available LULC products. Experimental evaluation is performed on high-resolution airborne SAR data, captured over the German Wadden Sea. The results demonstrate that the proposed model produces smooth and accurate classification maps. A comparison with a single-branch FCN model indicates that the appropriate integration of interferometric coherence enables the improvement of classification performance

Strategies for PS processing of large sentinel-1 datasets

Several advanced DInSAR techniques have been used to map surface deformations due to volcanism, active tectonics, landslides, subsidence, and uplift as well as to monitor the deformation of critical infrastructure such as bridges and dams. Recently, studies have explored the potential of these techniques to be integrated into a permanently operating monitoring system. ESA’s Sentinel-1 satellites have been providing SAR images for such a purpose since 2014. Nowadays, it is easy to access more than 230 SAR images of any area of interest, and update this dataset every six days with a new image. Due to the high frequency of the data acquisition, the question arises on how to best handle such a dataset. Is it suitable to always consider the whole available dataset or would a partial processing of the dataset and combining the results at a later point be more appropriate? To answer these questions, three different processing strategies are investigated in this paper. The first is a continuously growing dataset and for the second and third strategy, the dataset was divided into sub-stacks with and without overlap. In this study, the key parameters of each strategy are analyzed. In addition, the size of the sub-stacks is varied and the results are compared

PSDefoPAT—Persistent Scatterer Deformation Pattern Analysis Tool

Persistent Scatterer Deformation Pattern Analysis Tool, for short PSDefoPAT, was designed to assign each measuring point of an advanced DInSAR data set a best-fitting time series model based on its displacement time series. In this paper, we will outline the operating principles of the tool. The periodic and trend components of a time series model are separately determined based on hypothesis tests. The periodic component is fitted as a sine function, and for the trend component, linear, quadratic, and piecewise linear regression models are considered. Additionally, the tool assesses the goodness-of-fit for each model in the form of the adjusted coefficient of determination $R^2_{adj}$ value. The tool works fully automatically and thus facilitates the analysis of large data sets, which are becoming more available to the public due to services such as the European Ground Motion Service. Additionally, we demonstrate the capabilities of PSDefoPAT using four case studies characterized by different deformation mechanisms, various extents of active deformation area, and varying density of measuring points. In all cases, we successfully reveal information on the temporal behavior of the deformation not apparent in the typically presented mean deformation velocity maps

Особенности расчета отпускных в 2014 году

Расчет отпускных - одна из самых популярных операций у бухгалтеров по расчету заработной платы. Отпускные уверенно занимают второе место, уступая лишь непосредственно самой зарплате. Долгое время правила расчета отпускных оставались неизменными, но со 2 апреля 2014 года средний заработок определяют исходя из 29,3 дней, а не 29,4. Соответствующие поправки внесены в статью 139 Трудового кодекса Федеральным законом от 02.04.2014 № 55-ФЗ

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170

Comparison of small baseline interferometric SAR processors for estimating ground deformation

The small Baseline Synthetic Aperture Radar (SAR) Interferometry (SBI) technique has been widely and successfully applied in various ground deformation monitoring applications. Over the last decade, a variety of SBI algorithms have been developed based on the same fundamental concepts. Recently developed SBI toolboxes provide an open environment for researchers to apply different SBI methods for various purposes. However, there has been no thorough discussion that compares the particular characteristics of different SBI methods and their corresponding performance in ground deformation reconstruction. Thus, two SBI toolboxes that implement a total of four SBI algorithms were selected for comparison. This study discusses and summarizes the main differences, pros and cons of these four SBI implementations, which could help users to choose a suitable SBI method for their specific application. The study focuses on exploring the suitability of each SBI module under various data set conditions, including small/large number of interferograms, the presence or absence of larger time gaps, urban/vegetation ground coverage, and temporally regular/irregular ground displacement with multiple spatial scales. Within this paper we discuss the corresponding theoretical background of each SBI method. We present a performance analysis of these SBI modules based on two real data sets characterized by different environmental and surface deformation conditions. The study shows that all four SBI processors are capable of generating similar ground deformation results when the data set has sufficient temporal sampling and a stable ground backscatter mechanism like urban area. Strengths and limitations of different SBI processors were analyzed based on data set configuration and environmental conditions and are summarized in this paper to guide future users of SBI techniques

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR's airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/1700

CD24 Is Not Required for Tumor Initiation and Growth in Murine Breast and Prostate Cancer Models

CD24 is a small, heavily glycosylated, GPI-linked membrane protein, whose expression has been associated with the tumorigenesis and progression of several types of cancer. Here, we studied the expression of CD24 in tumors of MMTV-PyMT, Apc1572/T+ and TRAMP genetic mouse models that spontaneously develop mammary or prostate carcinoma, respectively. We found that CD24 is expressed during tumor development in all three models. In MMTV-PyMT and Apc1572T/+ breast tumors, CD24 was strongly but heterogeneously expressed during early tumorigenesis, but decreased in more advanced stages, and accordingly was increased in poorly differentiated lesions compared with well differentiated lesions. In prostate tumors developing in TRAMP mice, CD24 expression was strong within hyperplastic lesions in comparison with non-hyperplastic regions, and heterogeneous CD24 expression was maintained in advanced prostate carcinomas. To investigate whether CD24 plays a functional role in tumorigenesis in these models, we crossed CD24 deficient mice with MMTV-PyMT, Apc1572T/+ and TRAMP mice, and assessed the influence of CD24 deficiency on tumor onset and tumor burden. We found that mice negative or positive for CD24 did not significantly differ in terms of tumor initiation and burden in the genetic tumor models tested, with the exception of Apc1572T/+ mice, in which lack of CD24 reduced the mammary tumor burden slightly but significantly. Together, our data suggest that while CD24 is distinctively expressed during the early development of murine mammary and prostate tumors, it is not essential for the formation of tumors developing in MMTV-PyMT, Apc1572T/+ and TRAMP mice