July 2020
Intermediate to advanced
164 pages
3h 16m
English
Content preview from Deep Learning for Remote Sensing Images with Open Source Software
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This chapter was an insight to semantic segmentation on remote sensing images. We have introduced the semantic segmentation using a U-Net like architecture, a popular model targeting semantic segmentation of images.
We have built and used a small model to classify the buildings over one entire remote sensing image, without any limitation regarding its size, and in reasonable time.
We saw that, unlike the patch-based approach, semantic segmentation employs models that preserve the spatial resolution of the output. However, we have also understood that this kind of models must be trained from densely annotated images: the training step require patches of images that are fully annotated, i.e. each pixel of the image has a corresponding ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.