The purpose of the shapefile is to select some polygons and tag them with class labels, so that the application knows where to pick pixels to train itself. If you want a shapefile of the whole image footprint, you can use the output of ImageEnvelope application.

About projections, maybe have a look at tools such as gdalinfo and ogrinfo

they are great to have relevant information on images and shapefiles. If you want a reference about projections, you can check http://epsg.io/

Regards,
Guillaume

On 12/20/18 5:51 PM, bogdanul2003 wrote:
Hi,

I've tried different shapefiles with dimensions rangeing from a small part of the image to the entire image. I'm actually interested to consider the entire image as one class so I'm actually interested in a shapefile with 100x100 dimension which is the actual size of the image.
1. Is there a posibility to consider the entire image without using shapefiles ? In other words, can the framework be used without considering geospatial information, which in my case is irelevant ?
2. I'm a noob using tif files and shapefiles so for me the projections concept is unknown. I've attached all the files generated when I created the shapefile with QGIS. Can you please point me to a projections for dummies tutorial with QGIS ?

On Wednesday, December 19, 2018 at 6:03:19 PM UTC+1, Guillaume Pasero wrote:
Hi,

Have you checked that your shapefile and image properly overlap ?

Your image doesn't have any projection. Does your shapefile has one ?

Regards,
Guillaume

Quoting bogdanul2003 <[email protected]>:

> Hi,
>
> I'm trying to run this app with one tif image and one shape file (I
> attached both) but I get the fallowing error "No samples found in the
> inputs!". This is not happening if I use the examples in the Classification
> folder. I'm new to this framework and kind of stuck and don't know what is
> causing this, I tried to look in the source code and can't figure out why
> noSamples is never set to false. I generated the shapefile using QGIS and
> also I'm interested in the single class SVM classification.
>
> Thank you,
> Bogdan
>
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