updated 2018-19 final (1)

TECH PULSE 2018-2019
Image Segmentation: Segmentation is essential for image
analysis tasks. Semantic segmentation describes the process
of associating each pixel of an image with a class label,
(such as flower, person, road, sky, ocean, or car).
U-NET for Satellite Image Processing: The UNET was
developed by Olaf Ronneberger et al. for Bio Medical
Image Segmentation. The same can be applied for satellite
image segmentation. This approach gives high accuracy
with less training data.
A U-Net is like a convolution auto
encoder, But it also has skip-like connections with the
feature maps located before the bottleneck (compressed
embedding) layer, in such a way that in the decoder part
some information comes from previous layers, bypassing
the compressive bottleneck. Thus, in the decoder, data is
not only recovered from a compression, but is also
concatenated with the information’s state before it was
passed into the compression bottleneck so as to augment
context for the next decoding layers to come. That way, the
neural networks still learns to generalize in the compressed
latent representation (located at the bottom of the “U” shape
in the figure), but also recovers its latent generalizations to
a spatial representation with the proper per-pixel semantic
alignment in the right part of the U of the U-Net
Applications:
1. Yearly analysis of crops.
2. Amount of crop area generated.
3. Area under distruction due to calamities.
4. Area's that can be renowed for welfare.
Conclusion:
I conclude that by using UNET, we get high accuracy with
very limited data-set. It helps us to get a better result by
increasing quality of training dataset. So, over all U-is a
very efficient technique for image segmentation to extract
features from satellite in Images.