Export Training Data For Deep Learning The Difference In Brightness of Output Files

[sukruburakcetin]

Hello there,
When I try to use the Image Analyst tool Export Traning Data For Deep Learning, the exported tile outputs' come with an arranged level that their brightness is exaggerated.

What I mean is that the output tiles' color balance or luminance differentiation doesn't suit the original raster file. This is not a big issue since I examined the results are not being affected too much after I executed sample tasks for my deep learning needs. However, I would like to know what is the exact agent that is causing shifts in the brightness of the exported tile files.

I tried all configurations including extension modes, metadata format, reference system. All are leading the same output.

I added a sample region that clarifies the issue as you may be interested to get further info.

[DanPatterson]

what symbology are you using for the layer?  Do they look the same un clipped/stretched assuming you are using rgb and default symbology

[sukruburakcetin]

First of all thank you so much dear @DanPatterson .

To clarify the issue as much as detailed, I used 4 bands .tif imaginary and simply extracted the first 3 bands separately, then used composite band tool to reunite them without making any changes in symbology(default settings).

Then, I used Export Training Data for Deep Learning Tool as with the setup you mentioned in your previous message.

In short, the symbology I used has a percent clip stretch type (min 0,500 to max 0,500) with 0,9 gamma.

After your saying, this might be the issue, but when I want to display the 3 band composited image and compare it with the one that has 4 bands(original specimen), the color balance looks the same. 4 bands original imaginary has also same symbology(percent clip stretch type min 0,500 and max 0,500)

layer_symbology visual is added to the attachments to make it more pronounced. Thanks in advance.

[DanPatterson]

you should try the comparison without any clip or stretch is what I was suggesting since the range of values in each image will determine the high and low values and over what range to make adjustments