Leveraging optical and SAR data with a UU-Net for large-scale road extraction

Abstract Synthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

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JointNet: A Common Neural Network for Road and Building Extraction

Remote Sensing ◽

10.3390/rs11060696 ◽

2019 ◽

Vol 11 (6) ◽

pp. 696 ◽

Cited By ~ 13

Author(s):

Zhengxin Zhang ◽

Yunhong Wang

Keyword(s):

Neural Network ◽

Receptive Field ◽

Loss Function ◽

Large Scale ◽

Connectivity Pattern ◽

Automatic Extraction ◽

Network Module ◽

Building Extraction ◽

Road Extraction ◽

General Method

Automatic extraction of ground objects is fundamental for many applications of remote sensing. It is valuable to extract different kinds of ground objects effectively by using a general method. We propose such a method, JointNet, which is a novel neural network to meet extraction requirements for both roads and buildings. The proposed method makes three contributions to road and building extraction: (1) in addition to the accurate extraction of small objects, it can extract large objects with a wide receptive field. By switching the loss function, the network can effectively extract multi-type ground objects, from road centerlines to large-scale buildings. (2) This network module combines the dense connectivity with the atrous convolution layers, maintaining the efficiency of the dense connection connectivity pattern and reaching a large receptive field. (3) The proposed method utilizes the focal loss function to improve road extraction. The proposed method is designed to be effective on both road and building extraction tasks. Experimental results on three datasets verified the effectiveness of JointNet in information extraction of road and building objects.

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Operational Flood Detection Using Sentinel-1 SAR Data over Large Areas

Water ◽

10.3390/w11040786 ◽

2019 ◽

Vol 11 (4) ◽

pp. 786 ◽

Cited By ~ 13

Author(s):

Han Cao ◽

Hong Zhang ◽

Chao Wang ◽

Bo Zhang

Keyword(s):

Large Scale ◽

Region Growing ◽

Landsat 8 ◽

Weather Factors ◽

Jialing River ◽

Sar Data ◽

Flood Detection ◽

Multiresolution Segmentation ◽

Flooded Area ◽

Thresholding Algorithm

Unsupervised flood detection in large areas using Synthetic Aperture Radar (SAR) data always faces the challenge of automatic thresholding, because the histograms of large-scale images are unimodal, which thus makes it difficult to determine the threshold. In this paper, an iteratively multi-scale chessboard segmentation-based tiles selection method is introduced. This method includes a robust search procedure for tiles which obey bimodal Gaussian distribution, and a non-parametric histogram-based thresholding algorithm for thresholds identifying water areas. Then, the thresholds are integrated into the region-growing algorithm to obtain a consistent flood map. In addition, a classification refinement technique using multiresolution segmentation is proposed to address the omission in a heterogeneous flood area caused by water surface roughening due to weather factors (e.g., wind or rain). Experiments on the flooded area of Jialing River on July 2018 using Sentinel-1 images show a high classification accuracy of 99.05% through the validation of Landsat-8 data, indicating the validity of the proposed method.

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Analysis of synthetic aperture radar Data collected over the southwestern Greenland ice sheet

Journal of Glaciology ◽

10.1017/s002214300001577x ◽

1993 ◽

Vol 39 (131) ◽

pp. 119-132 ◽

Cited By ~ 46

Author(s):

K. C. Jezek ◽

M. R. Drinkwater ◽

J. P. Crawford ◽

R. Bindschadler ◽

R. Kwok

Keyword(s):

Synthetic Aperture Radar ◽

Large Scale ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

Synthetic Aperture ◽

Model Calculations ◽

Synthetic Aperture Radar Data ◽

Surface Patterns ◽

Sar Data ◽

Aperture Radar

AbstractAnalyses of the first aircraft multi-frequency, Polarimetric synthetic aperture radar (SAR) data acquired over the southwestern Greenland ice sheet are presented. Data were collected on 31 August 1989 by the Jet Propulsion Laboratory SAR using the NASA DC-8 aircraft. Along with curvilinear patterns associated with large-scale morphologic features such as crevasses, lakes and streams, frequency and polarization dependencies are observed in the P-, L-and C-band image products. Model calculations that include firn grain-size and volumetric water content suggest that tonal variations in and between the images are attributable to large-scale variations in the snow-and ice-surface characteristics, especially snow wetness. In particular, systematic trends in back-scatter strength observed at C-band across regions of changing snow wetness are suggestive of a capability to delineate boundaries between snow facies. Ice lenses and ice pipes are the speculated cause for similar trends in P-band back-scatter. Finally, comparison between SEASAT SAR data collected in 1978 and these airborne data collected in 1989 indicate a remarkable stability of surface patterns associated with the locations of supraglacial lake and stream systems.

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Multi-Temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi7100389 ◽

2018 ◽

Vol 7 (10) ◽

pp. 389 ◽

Cited By ~ 19

Author(s):

Wei He ◽

Naoto Yokoya

Keyword(s):

Deep Learning ◽

Large Scale ◽

Super Resolution ◽

Optical Image ◽

Optical Data ◽

Image Simulation ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Multi Temporal ◽

Sar Data

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.

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Height and large-scale height roughness information from CCRS interferometric SAR data of Arctic sea ice

Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1994.399559 ◽

2002 ◽

Cited By ~ 1

Author(s):

M.W.A. van der Kooij ◽

A.L. Gray ◽

M. Manore ◽

K.E. Mattar ◽

C.E. Livingstone ◽

...

Keyword(s):

Sea Ice ◽

Large Scale ◽

Scale Height ◽

Arctic Sea Ice ◽

Arctic Sea ◽

Sar Data ◽

Interferometric Sar

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Systematic and Automatic Large-Scale Flood Monitoring System Using Sentinel-1 SAR Data

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9323428 ◽

2020 ◽

Author(s):

Marco Chini ◽

Ramona Pelich ◽

Renaud Hostache ◽

Patrick Matgen ◽

Christian Bossung ◽

...

Keyword(s):

Monitoring System ◽

Large Scale ◽

Flood Monitoring ◽

Sar Data

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Rabi cropped area forecasting of parts of Banaskatha District,Gujarat using MRS RISAT-1 SAR data

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-1413-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 1413-1416 ◽

Cited By ~ 1

Author(s):

R. A. Parekh ◽

R. L. Mehta ◽

A. Vyas

Keyword(s):

Optical Sensors ◽

Large Scale ◽

Monsoon Season ◽

Scanning System ◽

Vegetation Density ◽

Data Set ◽

Multi Temporal ◽

Sar Data ◽

Rabi Crops ◽

Color Composite

Radar sensors can be used for large-scale vegetation mapping and monitoring using backscatter coefficients in different polarisations and wavelength bands. Due to cloud and haze interference, optical images are not always available at all phonological stages important for crop discrimination. Moreover, in cloud prone areas, exclusively SAR approach would provide operational solution. This paper presents the results of classifying the cropped and non cropped areas using multi-temporal SAR images. Dual polarised C- band RISAT MRS (Medium Resolution ScanSAR mode) data were acquired on 9thDec. 2012, 28thJan. 2013 and 22nd Feb. 2013 at 18m spatial resolution. Intensity images of two polarisations (HH, HV) were extracted and converted into backscattering coefficient images. Cross polarisation ratio (CPR) images and Radar fractional vegetation density index (RFDI) were created from the temporal data and integrated with the multi-temporal images. Signatures of cropped and un-cropped areas were used for maximum likelihood supervised classification. Separability in cropped and umcropped classes using different polarisation combinations and classification accuracy analysis was carried out. FCC (False Color Composite) prepared using best three SAR polarisations in the data set was compared with LISS-III (Linear Imaging Self-Scanning System-III) image. The acreage under rabi crops was estimated. The methodology developed was for rabi cropped area, due to availability of SAR data of rabi season. Though, the approach is more relevant for acreage estimation of kharif crops when frequent cloud cover condition prevails during monsoon season and optical sensors fail to deliver good quality images.

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