OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Zhao, Jiewen; Zhang, Zenghui; Yao, Wei; Datcu, Mihai; Xiong, Hao; Wang, Yu

doi:10.1109/jstars.2019.2954850

Cited by 43 publications

(44 citation statements)

References 59 publications

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“…Performance is, however, partially higher for Sentinel-1 HH+HV and Sentinel-2 combinations (>0.9; Table 4). It should be noted that previous studies (including [23,26,41,47,71]) have focused on VV+VH usage only in case of Sentinel-1 as this is the commonly available acquisition mode for IW over land.…”

Section: Target Classesmentioning

confidence: 99%

“…Specifically, deep learning is expected to allow identification of human footprint-related features and to deal with ambiguities. A range of remote sensing studies, including in urban environments, exist for these approaches [40,41].…”

Section: Introductionmentioning

confidence: 99%

“…Road monitoring applications based on SAR data are usually focusing on subsidence monitoring, but deep learning, specifically the UNet neural network architecture [46], has been shown to be of value for road extraction from Sentinel-1 backscatter amplitude [47]. Furthermore, the results of Zhao et al [41] for cities in China demonstrate the superior performance of deep learning for separation of urban targets from Sentinel-1.…”

Section: Introductionmentioning

confidence: 99%

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Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

et al. 2020

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Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads which are typical for gas and oil fields. A combination of results from both GBM (Sentinel-1 and -2 combined) and DL (Sentinel-2; Sentinel-1 optional) is therefore suggested for circumpolar mapping.

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Section: Target Classesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

et al. 2020

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“…The Sentinel-1 dataset contains 33358 image patches of SAR urban targets, covering 21 major cities of China, with 10 categories, 2 polarization modes and 4 formats. [7] In this research, 300 patches are sampled from each class, regarding the 2 polarization mode, respectively.…”

Section: Datasetsmentioning

confidence: 99%

Study Cases on Fast Compression Distance Based Data Visualization

Yao

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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In this paper, we develop a visualization tool to enhance the understanding of up to big data sets. Compared to classic data models which rely on the computing of the features (color, texture, etc.), this tool is fully feature free, as it processes directly on the data file. The Fast Compression Distance (FCD) and t-distributed Stochastic Neighbor Embedding (t-SNE) have been applied to visualize a large TerraSAR-X dataset which are annotated with up to three layers of hierarchical semantic labels, and a Sentinel-1 dataset with 10 annotated classes, in VV and VH polarization modes. We analyze the visualization results in manifold space, and try to understand and interpret them with the available semantic labels. The visualization interpretation is based on a vega-style interactive tool, which allows user zoom in, zoom out for processing large amount of data points.

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“…In the Sentinel-1 dataset, the number of classes varies from 5 to 10 specific to the image location and local urban structures. Zhao et al [3] presented the OpenSARUrban dataset for urban interpretation which covers areas of 21 major cities in China. There are 5 functional classes and a total of 10 classes in OpenSARUrban.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Separability of SAR Classification Dataset in Open Set Conditions

Liao

Datcu

Zhang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In Synthetic Aperture Radar (SAR) image classification applications, there are three categories of data, including training and benchmark data with fixed classes, as well as actual data in practical applications. A real problem comes that there exist unknown classes not included in training and benchmark data, which is defined as the open set condition. However, little work on recognizing unknown classes and analyzing the separability of SAR datasets has been developed. Motivated by this observation, this paper demonstrates the difficulty of practical classification and analyzes SAR dataset separability in open set conditions. In this paper, SAR separability analyzer (SAR-SA) is proposed to model each known class as a multivariate Gaussian distribution. SAR-SA can classify the known classes and recognize the samples locating in each known distribution with low probabilities as unknown. Besides, SAR dataset-wise separability index (DSI) and class-wise separability index (CSI) are defined to quantify the separability in open set conditions at the dataset level and class level. DSI and CSI are effective indicators of the difficulty of SAR classification datasets. Extensive experimental results demonstrate that the DSI in open set conditions is nearly half of that in supervised conditions. Dataset with low DSI is hard to realize accurate classification in open set conditions. At the class level, even though the SAR image classes are semantically different from each other, there exists more or less overlap between the distributions of supervised known classes and unknown classes. Classes with low CSI are harder to be correctly classified and recognized.

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OpenSARUrban: A Sentinel-1 SAR Image Dataset for Urban Interpretation

Cited by 43 publications

References 59 publications

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

Study Cases on Fast Compression Distance Based Data Visualization

Analyzing the Separability of SAR Classification Dataset in Open Set Conditions

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