Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 02/08/2022 - 17:46
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

The optical remote sensing (ORS) ship dataset contains eight ship classes (i.e., bulk carrier, car carrier, cargo, chemical tanker, container, dredge, oil tanker, tug) with a total of 8678 pictures. All pictures are collected using Google Earth with sub-meter resolution and corresponding class information are matched with the official website[http://www.marinetraffic.com/]

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The geological and hydrological pressures were generally neglected primarily due to the challenging nature of the monitoring task in an underwater setting, particularly over a large-scale region. Using repeated multibeam sonar measurements, we developed an uncertainty-based approach for interpreting the geomorphic change of the Lion City, an ancient Chinese city that is submerged in Qiandao lake.

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The geological and hydrological pressures were generally neglected primarily due to the challenging nature of the monitoring task in an underwater setting, particularly over a large-scale region. Using repeated multibeam sonar measurements, we developed an uncertainty-based approach for interpreting the geomorphic change of the Lion City, an ancient Chinese city that is submerged in Qiandao lake.

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hyperspectral images including Indian Pines, Salinas and the University of Pavia.

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None

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Influence of PET bottle mattress in fly ash fill following methodology were studied experimentally and analytically. Positive behaviour were observed interms of load- settlement.

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The lack of quality label data is considered one of the main bottlenecks for training machine and deep learning models. Weakly supervised learning using incomplete, coarse, or inaccurate data is an alternative strategy to overcome the scarcity of training data. We trained a U-Net model for segmenting Buildings’ footprints from a high-resolution digital elevation model, using existing label data from the open-access Microsoft building footprints data set.

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Single band homogeneous and heterogenoues dataset for change detection. The dataset includes different type of sensors an variety of cases sucha as floods, contructions, fire, ice-melting and so on.

Here you will find a demo of the work  in one dataset. To acces to the full dataset please follow the github link.

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