A rapid mapping approach to quantify damages caused by the 2003 bam earthquake using high resolution multitemporal optical images

2015 ◽  
Author(s):  
Daniela Faur ◽  
Mihai Datcu
Keyword(s):  
High Resolution ◽  
Bam Earthquake ◽  
Rapid Mapping ◽  
Mapping Approach ◽  
Optical Images

scholarly journals SAR AND OBLIQUE AERIAL OPTICAL IMAGE FUSION FOR URBAN AREA IMAGE SEGMENTATION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 639-642 ◽  
Author(s):  
J. Fagir ◽  
A. Schubert ◽  
M. Frioud ◽  
D. Henke
Keyword(s):  
Image Segmentation ◽  
High Resolution ◽  
Single Channel ◽  
Research Area ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Surface Model ◽  
Digital Surface Model ◽  
Oblique View ◽  
Optical Images

The fusion of synthetic aperture radar (SAR) and optical data is a dynamic research area, but image segmentation is rarely treated. While a few studies use low-resolution nadir-view optical images, we approached the segmentation of SAR and optical images acquired from the same airborne platform – leading to an oblique view with high resolution and thus increased complexity. To overcome the geometric differences, we generated a digital surface model (DSM) from adjacent optical images and used it to project both the DSM and SAR data into the optical camera frame, followed by segmentation with each channel. The fused segmentation algorithm was found to out-perform the single-channel version.

scholarly journals DCN-Based Spatial Features for Improving Parcel-Based Crop Classification Using High-Resolution Optical Images and Multi-Temporal SAR Data

2019 ◽  
Vol 11 (13) ◽  
pp. 1619 ◽  
Author(s):  
Zhou Ya’nan ◽  
Luo Jiancheng ◽  
Feng Li ◽  
Zhou Xiaocheng
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Short Term Memory ◽  
Multivariate Time Series ◽  
Hunan Province ◽  
Spatial Features ◽  
Optical Images ◽  
Multi Temporal ◽  
Crop Classification ◽  
Sar Data

Spatial features retrieved from satellite data play an important role for improving crop classification. In this study, we proposed a deep-learning-based time-series analysis method to extract and organize spatial features to improve parcel-based crop classification using high-resolution optical images and multi-temporal synthetic aperture radar (SAR) data. Central to this method is the use of multiple deep convolutional networks (DCNs) to extract spatial features and to use the long short-term memory (LSTM) network to organize spatial features. First, a precise farmland parcel map was delineated from optical images. Second, hundreds of spatial features were retrieved using multiple DCNs from preprocessed SAR images and overlaid onto the parcel map to construct multivariate time-series of crop growth for parcels. Third, LSTM-based network structures for organizing these time-series features were constructed to produce a final parcel-based classification map. The method was applied to a dataset of high-resolution ZY-3 optical images and multi-temporal Sentinel-1A SAR data to classify crop types in the Hunan Province of China. The classification results, showing an improvement of greater than 5.0% in overall accuracy relative to methods without spatial features, demonstrated the effectiveness of the proposed method in extracting and organizing spatial features for improving parcel-based crop classification.

Classifications of High Resolution Optical Images using Supervised Algorithms

2018 ◽  
Author(s):  
Balnarsaiah Battula ◽  
Laxminarayana Parayitam ◽  
T. S. Prasad ◽  
Penta Balakrishna ◽  
Chandrasekhar Patibandla
Keyword(s):  
High Resolution ◽  
Optical Images

scholarly journals Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model

2018 ◽  
Vol 10 (9) ◽  
pp. 1459 ◽  
Author(s):  
Ying Sun ◽  
Xinchang Zhang ◽  
Xiaoyang Zhao ◽  
Qinchuan Xin
Keyword(s):  
High Resolution ◽  
Active Contour ◽  
Active Contour Model ◽  
Remote Sensing Data ◽  
Lidar Data ◽  
Boundary Extraction ◽  
Contour Model ◽  
Optical Images ◽  
Building Footprint ◽  
Salt And Pepper

Identifying and extracting building boundaries from remote sensing data has been one of the hot topics in photogrammetry for decades. The active contour model (ACM) is a robust segmentation method that has been widely used in building boundary extraction, but which often results in biased building boundary extraction due to tree and background mixtures. Although the classification methods can improve this efficiently by separating buildings from other objects, there are often ineluctable salt and pepper artifacts. In this paper, we combine the robust classification convolutional neural networks (CNN) and ACM to overcome the current limitations in algorithms for building boundary extraction. We conduct two types of experiments: the first integrates ACM into the CNN construction progress, whereas the second starts building footprint detection with a CNN and then uses ACM for post processing. Three level assessments conducted demonstrate that the proposed methods could efficiently extract building boundaries in five test scenes from two datasets. The achieved mean accuracies in terms of the F1 score for the first type (and the second type) of the experiment are 96.43 ± 3.34% (95.68 ± 3.22%), 88.60 ± 3.99% (89.06 ± 3.96%), and 91.62 ±1.61% (91.47 ± 2.58%) at the scene, object, and pixel levels, respectively. The combined CNN and ACM solutions were shown to be effective at extracting building boundaries from high-resolution optical images and LiDAR data.

High-resolution infrared molecular hydrogen images and optical images of Herbig-Haro object 43

1988 ◽  
Vol 334 ◽  
pp. L99 ◽  
Author(s):  
Richard D. Schwartz ◽  
Donald G. Jennings ◽  
Peredur M. Williams ◽  
Martin Cohen
Keyword(s):  
High Resolution ◽  
Molecular Hydrogen ◽  
Optical Images

scholarly journals Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

2014 ◽  
Vol 14 (7) ◽  
pp. 1835-1841 ◽  
Author(s):  
A. Manconi ◽  
F. Casu ◽  
F. Ardizzone ◽  
M. Bonano ◽  
M. Cardinali ◽  
...  
Keyword(s):  
High Resolution ◽  
Synthetic Aperture Radar ◽  
Southern Italy ◽  
Surface Deformation ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Main Road ◽  
Rapid Mapping ◽  
X Band ◽  
Sar Data

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.

scholarly journals Rapid mapping application of vegetated terraces based on high resolution airborne LiDAR

2018 ◽  
Vol 9 (1) ◽  
pp. 970-985 ◽  
Author(s):  
Danilo Godone ◽  
Daniele Giordan ◽  
Marco Baldo
Keyword(s):  
High Resolution ◽  
Airborne Lidar ◽  
Rapid Mapping

scholarly journals A Multi-Scale Mapping Approach Based on a Deep Learning CNN Model for Reconstructing High-Resolution Urban DEMs

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1369
Author(s):  
Ling Jiang ◽  
Yang Hu ◽  
Xilin Xia ◽  
Qiuhua Liang ◽  
Andrea Soltoggio ◽  
...  
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Cost Effective ◽  
Reconstruction Method ◽  
Low Resolution ◽  
Multi Scale ◽  
Mapping Approach ◽  
Assessment Approach ◽  
Urban Topography ◽  
Complex Features

The scarcity of high-resolution urban digital elevation model (DEM) datasets, particularly in certain developing countries, has posed a challenge for many water-related applications such as flood risk management. A solution to address this is to develop effective approaches to reconstruct high-resolution DEMs from their low-resolution equivalents that are more widely available. However, the current high-resolution DEM reconstruction approaches mainly focus on natural topography. Few attempts have been made for urban topography, which is typically an integration of complex artificial and natural features. This study proposed a novel multi-scale mapping approach based on convolutional neural network (CNN) to deal with the complex features of urban topography and to reconstruct high-resolution urban DEMs. The proposed multi-scale CNN model was firstly trained using urban DEMs that contained topographic features at different resolutions, and then used to reconstruct the urban DEM at a specified (high) resolution from a low-resolution equivalent. A two-level accuracy assessment approach was also designed to evaluate the performance of the proposed urban DEM reconstruction method, in terms of numerical accuracy and morphological accuracy. The proposed DEM reconstruction approach was applied to a 121 km2 urbanized area in London, United Kingdom. Compared with other commonly used methods, the current CNN-based approach produced superior results, providing a cost-effective innovative method to acquire high-resolution DEMs in other data-scarce regions.

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.

High resolution optical images

UQ eSpace ◽  
2015 ◽  
Author(s):  
Shiliang Wang ◽  
Lizhen Hou ◽  
Hongtao Xie ◽  
Han Huang
Keyword(s):  
High Resolution ◽  
Optical Images
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