Array DBMS and Satellite Imagery: Towards Big Raster Data in the Cloud

2017 ◽  
pp. 267-279 ◽  
Author(s):  
Ramon Antonio Rodriges Zalipynis ◽  
Evgeniy Pozdeev ◽  
Anton Bryukhov
Keyword(s):  
Satellite Imagery ◽  
Raster Data

scholarly journals Automatic Proba-V Processor: TREX—Tool for Raster Data Exploration

2019 ◽  
Vol 11 (21) ◽  
pp. 2538 ◽  
Author(s):  
Joanna Suliga ◽  
Joy Bhattacharjee ◽  
Jarosław Chormański ◽  
Ann van Griensven ◽  
Boud Verbeiren
Keyword(s):  
Open Source ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Original Data ◽  
Data Exploration ◽  
Plant Canopy ◽  
Raster Data ◽  
Northeastern Poland

The processing tool TREX, standing for ‘Tool for Raster data EXploration’ is presented and evaluated in the Biebrza wetlands in northeastern Poland. TREX was designed for the automatization of processing satellite data from the Proba-V satellite into maps of NDVI or LAI in any defined by the user projection, spatial resolution, or extent. The open source and access concept of TREX encourages the potential community of users to collaborate, develop, and integrate the tool with other satellite imagery and models. TREX reprojects, shifts, and resamples original data obtained from the Proba-V satellite to deliver reliable maps of NDVI and LAI. Validation of TREX in Biebrza wetlands resulted in correlations between 0.79 and 0.92 for NDVI data (measured with ASD Field Spec 4) and 0.92 for LAI data (measured with LiCOR—LAI-2000 Plant Canopy Analyzer).

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

Mask R-CNN based segmentation method for satellite imagery of photovoltaics generation systems

2020 ◽  
Author(s):  
SiMing Liang ◽  
FengYang Qi ◽  
YiFan Ding ◽  
Rui Cao ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Segmentation Method
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