scholarly journals Earthquake-Induced Landslide Mapping for the 2018 Hokkaido Eastern Iburi Earthquake Using PALSAR-2 Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2351 ◽  
Author(s):  
Yusupujiang Aimaiti ◽  
Wen Liu ◽  
Fumio Yamazaki ◽  
Yoshihisa Maruyama
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Ground Truth Data ◽  
Active Sensor ◽  
Optical Images ◽  
Comparison Results ◽  
Landslide Mapping ◽  
Aperture Radar

Timely information about landslides during or immediately after an event is an invaluable source for emergency response and management. Using an active sensor, synthetic aperture radar (SAR) can capture images of the earth’s surface regardless of weather conditions and may provide a solution to the problem of mapping landslides when clouds obstruct optical imaging. The 2018 Hokkaido Eastern Iburi earthquake (Mw 6.6) and its aftershocks not only caused major damage with severe loss of life and property but also induced many landslides across the area. To gain a better understanding of the landslides induced by this earthquake, we proposed a method of landslide mapping using pre- and post-event Advanced Land Observation Satellite 2 Phased Array L-band Synthetic Aperture Radar 2 (ALOS-2 PALSAR-2) images acquired from both descending and ascending orbits. Moreover, the accuracy of the classification results was verified by comparisons with high-resolution optical images, and ground truth data (provided by GSI, Japan). The detected landslides show a good match with the reference optical images by visual comparison. The quantitative comparison results showed that a combination of the descending and ascending intensity-based landslide classification had the best accuracy with an overall accuracy and kappa coefficient of 80.1% and 0.45, respectively.

scholarly journals An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 819-826 ◽  
Author(s):  
N. Milisavljevic ◽  
D. Closson ◽  
F. Holecz ◽  
F. Collivignarelli ◽  
P. Pasquali
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Ground Truth Data ◽  
Synthetic Aperture Radar Data ◽  
Before And After ◽  
The Impact ◽  
Aperture Radar

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. <br><br> The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. <br><br> A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

scholarly journals Snow density retrieval using SAR data: algorithm validation and applications in part of North Western Himalaya

2013 ◽  
Vol 7 (3) ◽  
pp. 1927-1960 ◽  
Author(s):  
P. K. Thakur ◽  
R. D. Garg ◽  
S. P. Aggarwal ◽  
P. K. Garg ◽  
J. Shi ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Western Himalaya ◽  
Synthetic Aperture ◽  
Snow Density ◽  
Ground Truth Data ◽  
Array Type ◽  
Runoff Modeling ◽  
Sar Data ◽  
Aperture Radar

Abstract. The current study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the dry snow density in Manali sub-basin of Beas River located in state of Himachal Pradesh, India. SAR data from Radarsat-2 (RS2), Environmental Satellite (ENVISAT), Advanced Synthetic Aperture Radar (ASAR) and Advanced Land Observing Satellite (ALOS)-Phased Array type L-band Synthetic Aperture Radar (PALSAR) have been used. The SAR based inversion models were implemented separately for fully polarimetric RS2, PALSAR and dual polarimetric ASAR Alternate polarization System (APS) datasets in Mathematica and MATLAB software and have been used for finding out dry snow dielectric constant and snow density. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. Overall accuracy in terms of R2 value and Root Mean Square Error (RMSE) was calculated as 0.85 and 0.03 g cm−3 for snow density based on the ground truth data. The retrieved snow density is highly useful for snow avalanche and snowmelt runoff modeling related studies of this region.

APPLIED USE OF HIGH RESOLUTION IMAGERY FOR HUMAN SAFETY AND ENVIRONMENT IN SOUTH SUMATRA, INDONESIA

2005 ◽  
Vol 2005 (1) ◽  
pp. 819-823
Author(s):  
Sarah Terry ◽  
Khalid A. Soofi ◽  
Yuli Kwenandar ◽  
Bill Mcintosh
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Spectral Resolution ◽  
Oil And Gas ◽  
Synthetic Aperture ◽  
Vegetation Types ◽  
Response Planning ◽  
Optical Images ◽  
Human Habitat ◽  
Aperture Radar

ABSTRACT The availability of extremely high resolution images offers an unprecedented opportunity to use such images to monitor, maintain and ultimately preserve and rehabilitate the natural environment throughout the life cycle of oil and gas projects. The variety of images available range from optical images such as Landsat ETM1 imagery (14.25 meter/pixel), IKONOS2 imagery (1 meter/pixel) and QuickBird3 imagery (0.6 meter/pixel). These optical images have sufficient spatial and spectral resolution to detect different vegetation types (e.g. old growth vs. new plantations), cleared vegetation caused by logging or human habitat expansion, burned areas due to fire and vegetation stress caused by spills from oil pipelines or storage vessels. These images are also useful for identifying potential pollutant sources such as abandoned wells, old drilling pits or other remediation targets, as well as potential pollutant receptors. Areas which have perpetual cloud cover, such as South Sumatra, of Indonesia, can be monitored using Synthetic Aperture Radar (e.g. European Space Agency's Synthetic Aperture Radar and RadarSat International of Canada). Although a typical SAR does not have the spectral resolution of optical sensors, it does have the advantage of seeing through clouds. The radar backscatter is sensitive to surface roughness and Dielectric Constant which can be used quite effectively to discriminate major vegetation types. These images, when combined with normal GIS tools, take us beyond simple monitoring, to generating predictive tools for planning future sites for drilling wells and placement of facilities such as pipelines and roads. This paper will focus on the use of these techniques for oil spill response planning in South Sumatra, while taking note of other applications of remote sensing and GIS to oil and gas operations in the regional environment.

scholarly journals Evaluation of the change in synthetic aperture radar imaging using transfer learning and residual network

2021 ◽  
Vol 45 (4) ◽  
pp. 600-607
Author(s):  
I. Hamdi ◽  
Y. Tounsi ◽  
M. Benjelloun ◽  
A. Nassim
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Transfer Learning ◽  
Ground Truth ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Residual Network ◽  
Edge Preservation ◽  
Radar Images ◽  
Aperture Radar

Change detection from synthetic aperture radar images becomes a key technique to detect change area related to some phenomenon as flood and deformation of the earth surface. This paper proposes a transfer learning and Residual Network with 18 layers (ResNet-18) architecture-based method for change detection from two synthetic aperture radar images. Before the application of the proposed technique, batch denoising using convolutional neural network is applied to the two input synthetic aperture radar image for speckle noise reduction. To validate the performance of the proposed method, three known synthetic aperture radar datasets (Ottawa; Mexican and for Taiwan Shimen datasets) are exploited in this paper. The use of these datasets is important because the ground truth is known, and this can be considered as the use of numerical simulation. The detected change image obtained by the proposed method is compared using two image metrics. The first metric is image quality index that measures the similarity ratio between the obtained image and the image of the ground truth, the second metrics is edge preservation index, it measures the performance of the method to preserve edges. Finally, the method is applied to determine the changed area using two Sentinel 1 B synthetic aperture radar images of Eddahbi dam situated in Morocco.

ACFNet: A Feature Fusion Network for Glacial Lake Extraction Based on Optical and Synthetic Aperture Radar Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5091
Author(s):  
Jinxiao Wang ◽  
Fang Chen ◽  
Meimei Zhang ◽  
Bo Yu
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Glacial Lake ◽  
Landsat 8 ◽  
Glacial Lakes ◽  
Sar Images ◽  
Radar Images ◽  
Optical Images ◽  
Sar Data ◽  
Aperture Radar

Glacial lake extraction is essential for studying the response of glacial lakes to climate change and assessing the risks of glacial lake outburst floods. Most methods for glacial lake extraction are based on either optical images or synthetic aperture radar (SAR) images. Although deep learning methods can extract features of optical and SAR images well, efficiently fusing two modality features for glacial lake extraction with high accuracy is challenging. In this study, to make full use of the spectral characteristics of optical images and the geometric characteristics of SAR images, we propose an atrous convolution fusion network (ACFNet) to extract glacial lakes based on Landsat 8 optical images and Sentinel-1 SAR images. ACFNet adequately fuses high-level features of optical and SAR data in different receptive fields using atrous convolution. Compared with four fusion models in which data fusion occurs at the input, encoder, decoder, and output stages, two classical semantic segmentation models (SegNet and DeepLabV3+), and a recently proposed model based on U-Net, our model achieves the best results with an intersection-over-union of 0.8278. The experiments show that fully extracting the characteristics of optical and SAR data and appropriately fusing them are vital steps in a network’s performance of glacial lake extraction.

Synthetic aperture radar image segmentation using supervised artificial neural network

2020 ◽  
Vol 16 (4) ◽  
pp. 397-408
Author(s):  
R. Lalchhanhima ◽  
Goutam Saha ◽  
Morrel V.L. Nunsanga ◽  
Debdatta Kandar
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Intensity Information ◽  
Ground Truth Image ◽  
Aperture Radar

Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore, the segmentation process can not directly rely on the intensity information alone, but it must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use supervised information about regions for segmentation criteria in which ANN is employed to give training on the basis of known ground truth image derived. Three different features are employed for segmentation, first feature is the original image, second feature is the roughness information and the third feature is the filtered image. The segmentation accuracy is measured against the Difficulty of Segmentation (DoS) and Cross Region Fitting (CRF) methods. The performance of our algorithm has been compared with other proposed methods employing the same set of data.

scholarly journals Assessment of Sentinel-1A/B SAR Derived Ocean Wind Speeds against HY-2B Scatterometer in the Presence of Ocean Swells

2021 ◽  
Vol 299 ◽  
pp. 01001
Author(s):  
He Wang ◽  
Chaoying Shi ◽  
Jianhua Zhu
Keyword(s):  
Synthetic Aperture Radar ◽  
Wave Mode ◽  
Synthetic Aperture ◽  
Wind Speeds ◽  
Comparison Results ◽  
Ocean Winds ◽  
Spatio Temporal ◽  
Ocean Wind ◽  
Good Agreement ◽  
Aperture Radar

Sentinel-1A/B satellites operating in wave mode provide ocean winds dataset on a continuous and global basis. In this study, wind speeds derived from Sentinel-1A/B wave mode imagery from November 2018 to October 2020 are evaluated against HSCAT scatterometer aboard Chinese satellite HY-2B. Here, due to the close equatorial crossing times between Sentinel-1 and HY-2B, the spatio-temporal criteria of 50 km and 30 min yield large amount match-ups. Comparison results show a good agreement between wind speeds derived from the two types of radars: synthetic aperture radar and scatterometer. Impact of the presence of pure swell on the evaluation results is also discussed.

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