1. Hyperspectral Image Classification Using ResNeXt with Squeeze and Excitation Block
Jiayu Wang

2. Executive Summary
Project Intention:
Classify hyperspectral images between classes of almond, amber, shell, stone, and wood chip.
Accomplished tasks:
what tasks have been or will be performed before turning in the report (one or a couple of bullets),
your brief assessment of the level of success of your project compared to what was proposed in the proposal. (one bullet)

3. Approaches

4. Approaches

5. Approaches Data source:
Raw hyperspectral images are from the company I am interning with, Middleton Spectral Vision.
Data preprocessing:
Python program to convert all raw data into Numpy arrays and then format it as the input to the network.
Neural network architecture:
First developed ResNet with S&E block by myself in Python, using TensorFlow.
But failed due to exhausting debugging process.
Used an existing implementation of ResNeXt and modified it.
Platform:
The network is trained and evaluated using Euler.

6. Results
Project is still under developing. The project will be finished, and this slide will be filled before presentation.

7. Discussion
Project is still under developing. The project will be finished, and this slide will be filled before presentation.