data at https://filesender.funet.fi/?s=download&token=d05d4253-ef08-439e-968f-a10ef84877a4
Result: Won through using method from Vaisala
Delete, delete and reconstruct radar interferences.
Digitalize radar values and locate the img pixels on real locations (lat/lng of pixels) --> because of radar errors Detect interferences Delete interferences Correct interferences Save data into a file in netCDF format.
Real radar data of 5 months saptial resolution of 1km per pixel sampling rate of 10 minutes --> maybe interferences doesnt change between the images
ba, pm, va --> locations then inside is each day with 10 min intervals
each image from each area kinda overlap with each other --> u can correlate and check for interferences. dry: jan, feb wet: apri, may
last half of may for testing is the best
missing images is possible
Simple reading with pillow and cropping with pillow (including the radar circle).
Methods Tried
- Traditional CV (Canny Edge Detection, Hough Line Transformation and Custom Rule based Approach)
- Deep Learning
- Azimuth Radial Differential detection + Signal Professing (This worked very well )