Developed as a part of B.Tech Dissertation.

Code Manual : https://drive.google.com/open?id=1hLnw2yNUpvKDbErnlKUZbUSA2DjESjyA

Thesis soft copy : https://drive.google.com/open?id=1PQTapftFkTBi9-NZ1ixLuPSGxkn5h-ZB

Hyspeclib is a helper library written in python language on top of Tensorflow backend for analysis of hyperspectral images and perform classification task using supervised deep learning algorithm.

Hyspeclib includes helper functions for performing preprocessing on raw images such as noisy bands identification and removal. It provides utility for extracting spectral signatures and assigning class labels to it from ROI images selected using ENVI for supervised learning task. Training data can be visualised in 2D and quality of training dataset can be improved by excluding outliers using hyspeclib modules. Two dimensionality techniques can be implemented on any hyperspectral image for selecting optimal number of bands. Band reduction not only reduces the size of image but also reduces the processing time and improves the accuracy. It is also possible to measure class to class separability using selected band combination with JM-Distance algorithm provided here. Selecting large number of training points for each class ( for e.g crops ) manually using softwares is time consuming. Using data augmentation utility provided by hyspeclib, large number of high quality training samples can be selected automatically from the image itself provided few manually selected samples.

Deep learning requires a well tuned model in terms of optimal number of layers, optimal number of nodes in each layer, proper learning rate and many other hyper parameters. Hyspeclib provides a very easy way to design a network with desired parameters, desired number of layer and nodes for experimentation to achieve best classification accuracy based on images. Other than classification, validation is also important aspect in any supervised learning algorithm. Hyspeclib can calculate overall accuracy, average class accuracy, confusion matrix, kappa coefficient for training and blind site (testing data).

Hyspeclib is designed run on both GPU based and CPU based systems with python and required packages installed. For images having larger size than RAM (Computer memory), images are processed in smaller blocks so that entire algorithm can run on entire image without memory issues. Colourful classification mask can be generated and saved for hyperspectral image to visualise predicted class for given region of image.

@INPROCEEDINGS{8897897,
author={H. {Patel} and N. {Bhagia} and T. {Vyas} and B. {Bhattacharya} and K. {Dave}},
booktitle={IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},
title={Crop Identification and Discrimination Using AVIRIS-NG Hyperspectral Data Based on Deep Learning Techniques},
year={2019},
volume={},
number={},
pages={3728-3731},}