IIRS-PROJECT

This is the project performed at Indian Institute Of Remote Sensing (IIRS), Dehradun. (ISRO)

TOPIC : MULTISPECTRAL SATELLITE IMAGE SEGMENTATION USING THE SEG-NET DEEP LEARNING ARCHITECTURE

Dataset is downloaded from https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/data

OBJECTIVE: Semantic segmentation to detect and classify the types of objects found in these regions.

OVERVIEW: In this project I have used satellite image segmentation model (SegNet) in order to classify the types of objects found in these regions. There are 2 band images in the given dataset format that is 3 band and 16 band images where the image format is GeoTiff format.

The object types present in the geotiff format file are:

1. Buildings - large building, residential, non-residential, fuel storage facility, fortified building
2. Misc. Manmade structures
3. Road
4. Track - poor/dirt/cart track, footpath/trail
5. Trees - woodland, hedgerows, groups of trees, standalone trees
6. Crops - contour ploughing/cropland, grain (wheat) crops, row (potatoes, turnips) crops
7. Waterway
8. Standing water
9. Vehicle Large - large vehicle (e.g. lorry, truck,bus), logistics vehicle
10. Vehicle Small - small vehicle (car, van), motorbike

The SegNet model(Semantic Model). The core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer.

I have used Jaccard Coefficient to compare the memebrs for two sets to see which memebers are shared and which are distinct.

                J(x,y) = |x∩y| / |x∪y| where J(x,y) is the Jaccard Coefficent for x and y

RESULT: The accuracy obtained is 66.745% and the test accuracy is 73.97%