AeroRIT: A New Scene for Hyperspectral Image Analysis

This scene overlooking Rochester Institute of Technology was captured via Headwall Photonics Micro Hy-perspec E-Series CMOS sensor at an altitude of approximately 5,000 feat (0.4m GSD). The semantic map pixels were labelled with ENVI 4.8.2, using individual hyperspectral signatures and the geo-registered RGB images as references.

Structure of the repository

This repository is organized as:

Aerial Data This directory contains all the data required for training and analysis.
configs This directory contains a few examples for config files used in train and test functions.
helpers This directory the helper zoo for non-model related functions.
networks This directory the model zoo used in the paper.
savedmodels This directory will contain all the saved models post training.

Dataset

The scene images can be found here. The directory contains four files:

image_rgb - The RGB rectified hyperspectral scene.
image_hsi_radiance - Radiance calibrated hyperspectral scene sampled at every 10th band (400nm, 410nm, 420nm, .. 900nm).
image_hsi_reflectance - Reflectance calibrated hyperspectral scene sampled at every 10th band.
image_labels - Semantic labels for the entire AeroCampus scene.

Note: The above files only contain every 10th band from 400nm to 900nm. You can access the full versions of both radiance and reflectance at GoogleDrive.

Baselines Performance on the dataset

We train a series of networks with weighted cross-entropy loss and report the performance in terms of mean Intersection-over-Union:

Model	mIOU
SegNet	52.60
SegNet-m	59.08
Res-U-Net (6)	72.55
Res-U-Net (9)	70.88
U-Net	60.40
U-Net-m	70.62
U-Net-m + SE	75.35
U-Net-m + SE + PReLU act.	75.89
U-Net-m + SE + PReLU act. + Self Supervised Learning	76.40

All pretrained models can be found here. For re-checking or improving network architecture, please place the model files into savedmodels before executing any code.

Requirements

numpy

cv2 (opencv-python)

pytorch1.0.1

Pillow

We recommend to use Anaconda environment for running all sets of code. We have tested our code on Ubuntu 16.04 with Python 3.6.

Executing codes

Before running any files, execute sampling_data.py to obtain train, validation and test splits with 64 x 64 image chips.

Some of the important arguments used in train and test files are as follows:

Argument	Description
config-file	path to configuration file if present
bands	how many bands to sample from HSI imagery (3 -> RGB, 51 -> all) ?
hsi_c	use HSI radiance or reflectance for analysis ?
network_arch	which network architecture to use: Res-U-Net, SegNet or U-Net?
network_weights_path	path to save(d) network weights
use_cuda	use GPUs for processing or CPU?

Please refer to the corresponding files for indepth argument descriptions. To cross-verify the final reported result in the paper, test.py should be run with as follows: python test.py --config configs/eval-unetm-best.yaml

License

This scene dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

That the dataset comes “AS IS”, without express or implied warranty. Although every effort has been made to ensure accuracy, we (Rochester Institute of Technology) do not accept any responsibility for errors or omissions.
That you include a reference to the AeroCampus Dataset in any work that makes use of the dataset.
That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
You may not use the dataset or any derivative work for commercial purposes such as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
That all rights not expressly granted to you are reserved by us (Rochester Institute of Technology).

Citation

When using the dataset or code, please cite our paper arXiv:

@article{rangnekar2020aerorit,
  title={Aerorit: A new scene for hyperspectral image analysis},
  author={Rangnekar, Aneesh and Mokashi, Nilay and Ientilucci, Emmett J and Kanan, Christopher and Hoffman, Matthew J},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={58},
  number={11},
  pages={8116--8124},
  year={2020},
  publisher={IEEE}
}

Acknowledgements

The codebase is heavily based off pix2pix and pytorch-semseg. Both are great repositories - have a look!

aneesh3108 / AeroRIT