LargeScaleMeanShift

OTB Usage
1

Context

  • Segmentation of Spot7 image subset.

Configuration setup

My system: *Windows10
Version of the OTB: *7.1
I installed the OTB with: *the binaries,

Description of my issue

  • When i run my segmentation i have a “bad allocation” error : Caught std::exception during application execution: bad allocation
  • I putted as RAM limit : -ram 25000
  • I have 32 GO of available RAM
  • the segmentation use all my available RAM and crash befor Shapefile generating.
  • Image size is : 15360 x12875 pixel x 4 bands : UInt16

=> Segmentation commande : otbcli_LargeScaleMeanShift -in image -spatialr 7 -ranger 10 -minsize 100 -mode.vector.out image_seg.shp -ram 25000

What should I to do ?

Thanks
Mohamed.

2

Hello Mohamed. I am having a similar issue with LargeScaleMeanShift. Is the issue the same as I describe below??

‘LargeScaleMeanShift’ has failed with return status -1. Please refer to ‘LargeScaleMeanShift’ documentation and check log tab.

The crash appears to happen in the ‘Computing Stats on input image’ stage.

The log states:

(FATAL) LargeScaleMeanShift: Caught std::exception during application execution: bad allocation

1 Like
3

Hi Mark and thx for the reply,
I don’t think so. I put all the error message :

Estimated memory for full processing: 9806.98MB (avail.: 256 MB), optimal image partitioning: 39 blocks
2020-10-07 17:55:24 (INFO): Estimation will be performed in 40 blocks of 15360x322 pixels
Computing stats on input image …: 100% [**************************************************] (19s)
2020-10-07 18:09:56 (FATAL) LargeScaleMeanShift: Caught std::exception during application execution: bad allocation

4

Dear @kadirim,
Your issue looks very similar to what @wkcmark describes here. I will try to reproduce your bug, to understand what happens.
Best regards.
Julien.

1 Like
5

@julienosman if you need any further info on this let me know. I really need to get this up and running again.

6

Does the procedure work with a subset of the image? You could it to crop a much smaller portion and check if it runs then. If not then there is a deeper problem, if it runs through then it might be “simply” a memory problem

7

Dear @oldi
It seems be a memory problem even with 32 GO of Memory RAM.
I will try with Grass according to @wkcmark advice.
Regrads

8

@oldi yes it works on smaller areas for me. However in the previous version it was running fine with big dataset.

9

It could also be due to errors at the image boundary. You can try to make a much larger subset, with some decent distance to the image boundary.

10

@julienosman did you ever get anywhere with this? I’ve run into the error again.