Potential of Multi-resolution Satellite Imagery Products for Scale Variant Topographic Mapping

Author(s):  
Nidhi Gahlot ◽  
G. Prusty ◽  
Mrinmoy Dhara
2021 ◽  
pp. 939
Author(s):  
Winhard Tampubolon ◽  
Wolfgang Reinhardt ◽  
Franz Josef Behr

Due to its large area Large Scale Topographic Mapping (LSTM) for Indonesia requires acceleration strategies that must be innovative enough to take into account the production efficiency. Satellite-based technologies are still a preferable choice especially in conjunction with the security clearance and weather. Standards for the Very High-Resolution Satellite Imagery (VHRS) utilization are essential, especially in a situation where there are so many available sensors and processing methods implemented. Hence, the selection of a proper geometric correction method is fundamental in order to utilize the VHRS imagery as one source of geospatial data especially for LSTM production and updating purposes. For CSRT geometric correction, an orthorectification process is required, where this process requires input data from the Ground Control Point (TKT) and the Digital Elevation Model (DEM). Therefore, the Least Square Adjustment (LSA) method is implemented to be able to include 8-9 GCPs per-scene (orbital and sensor parameters) and the DEM with a maximum resolution 4 times of the VHRS imagery’s Ground Sampling Distance (GSD) in the process of producing VHRS orthoimages. In addition, the role of orbital and sensor parameters is also essential for the geometric correction because its relation to the Direct Georeferencing (DG) of each pixel by Rigorous Sensor Model (RSM) approach. However, in the situation where the reliable orbital and sensor parameters are not available, the Rational Function Model (RFM) can be used as an alternative solution for the geometric correction of VHRS imagery. This paper discusses the VHRS utilization with a comprehensive approach that can be implemented in a local coordinate system i.e. the Indonesian Geospatial Reference System for the production of the reliable VHRS imageries.


2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis

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.


Author(s):  
SiMing Liang ◽  
FengYang Qi ◽  
YiFan Ding ◽  
Rui Cao ◽  
Qiang Yang ◽  
...  

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