shyamsunder007/rit-18

High-Resolution Multispectral Dataset for Semantic Segmentation

RIT-18

High-Resolution Multispectral Dataset for Semantic Segmentation

Description

This repository contains the RIT-18 dataset we built for the semantic segmentation of remote sensing imagery. It was collected with the Tetracam Micro-MCA6 multispectral imaging sensor flown on-board a DJI-1000 octocopter. The main contributions of this dataset include 1) very-high resolution multispectral imagery from a drone, 2) six-spectral VNIR bands, and 3) 18 object classes (plus background) with a severely unbalanced class distribution. Details about its construction can be found in our paper.

If you use this dataset in a publication, please cite:

@article{kemker2017algorithms,
  title={Algorithms for Semantic Segmentation of Multispectral Remote Sensing Imagery using Deep Learning},
  author={Kemker, Ronald and Salvaggio, Carl and Kanan, Christopher},
  journal={arXiv preprint arXiv:1703.06452},
  year={2017}
}

Data Files

This repository contains the following files:

1. rit18_data_url: The URL to the current location of the data.
2. evaluate_rit18.py: The evaluation script used to score the predicition map
3. read_rit18.py: This script opens all of the data in the dataset.

The data, once downloaded, is ~3.0GB (1.58 GB compressed). It is a .mat file containing a dictionary of various elements including:

• 'train_data' : (7 x 9,393 x 5,642) numpy array containing the training ortho. The first six bands are the VNIR spectral bands and the 7th band is the mask of the orthomosaic.
• 'train_labels': (9,393 x 5,642) numpy array containing the training labels.
• 'val_data' : (7 x 8,833 x 6,918) numpy array containing the validation ortho. The first six bands are the VNIR spectral bands and the 7th band is the mask of the orthomosaic.
• 'val_labels' : (8,833 x 6,918) numpy array containing the validation labels.
• 'test_data' : (7 x 12,446 x 7,654) numpy array containing the testing ortho. The first six bands are the VNIR spectral bands and the 7th band is the mask of the orthomosaic.
• 'band_centers' : Spectral band centers
• 'band_center_units' : Units for 'band_centers'
• 'sensor' : Information about the sensor
• 'classes' : List of object classes
• 'info' : Various information about the dataset

Instructions

The dataset contain pixel-wise annotations for both the training and validation folds. Both sets of labels can be used to train a classifier. It is separated as a rough per-class split, but the validation fold does not contain the black and white wooden targets. This is because we want to evaluate our model's ability to perform low-shot learning.

The goal is to have the test labels available on the IEEE GRSS evaluation server. Until then, you can e-mail me your test predictions using the following format:

• Same spatial dimensions as the test image (12,446 x 7,654)
• uint8 datatype (smaller file)
• Either .mat (MATLAB) or .npy (Python) file format
• Compressed (so you don't kill my e-mail account)

I will use your predicitions on the evaluate_rit18.py script that I provided here and send you the output file. I will not score the area outside of the mask, but the background pixels ("class 0") will be scored. As soon as I get this up on the evaluation server, then the user will be able to do all of this themselves.

Contact

Author: Ronald Kemker

E-mail: [email protected]

Website: http://www.cis.rit.edu/~rmk6217/