Parallel Processing for Visualizing Remote Sensing Imagery Data Sets

2016 ◽  
pp. 285-306
Author(s):  
Safa Najim ◽  
Alaa Najim
Keyword(s):  
Remote Sensing ◽  
Parallel Processing ◽  
Data Sets ◽  
Remote Sensing Imagery ◽  
Imagery Data

scholarly journals Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2505 ◽  
Author(s):  
Crommelinck ◽  
Koeva ◽  
Yang ◽  
Vosselman
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Image Resolution ◽  
Aerial Imagery ◽  
Boundary Line ◽  
Data Sets ◽  
Remote Sensing Imagery ◽  
Manual Delineation ◽  
Segmentation Boundary ◽  
Boundary Classification

Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02–0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

scholarly journals Aerial Photo Utilization in Estimating Suspended Sediment in the Wuryantoro Watershed, Wonogiri

2004 ◽  
Vol 17 (2) ◽  
Author(s):  
Sugiharto Budi Santoso ◽  
Kuswaji Dwi Priyono ◽  
Alif Noor Anna
Keyword(s):  
Remote Sensing ◽  
Suspended Sediment ◽  
Field Measurements ◽  
Sediment Concentration ◽  
Rainfall Data ◽  
Remote Sensing Imagery ◽  
Remote Sensing Technique ◽  
Physical Data ◽  
Result Show ◽  
Imagery Data

Suspended sediment load flowing out from a watershed is normally predicated by analysis os suspended sediment of water sample, and the volume of suspended sediment be calculated based on sediment concentration and river discharge. Such field measurements need a lot of field data and they are time consuming. Another method for prediction of suspended sediment by using remote sensing imagery data and recorded rainfall data. The objective of this research is to 1) examine the capability of remote sensing technique to obtain the parameters of the physical data of land in the prediction of suspended sediment; 2) examine the accuracy of the model for prediction suspended sediment. This research is carried out in Wuryantoro watershed, Wonogiri. The main data to obtain the parameters of the physical data of land is infrared aerial photograph on scale 1 : 10.000. the method that used in this research is interpretation of remote sensing imagery data, combined with rainfall data. The result show that the accuracy of landuse is 88.5%, the accuracy of slope is 87.67%. the accuracy of the prediction of suspended sediment by model A3 87.07%, model C1 86.63%, model C2 90.57%, model A8 84.13%, model A9 80.1%, and model C4 78.6%.

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