Codes to calculate solar-sensor zenith and azimuth angles directly from hyperspectral images collected by UAV. Works only for UAVs that have high resolution GNSS/IMU unit.

Overview

Stargazers Forks Issues MIT License

UAV Solar-Sensor Angle Calculation


Table of Contents
  1. About The Project
  2. Getting Started
  3. Contributing
  4. License
  5. Contact
  6. Acknowledgments

About The Project

Calculating solar-sensor zenith and azimuth angles for hyperspectral images collected by UAVs are important in terms of conducting bi-directional reflectance function (BRDF) correction or radiative transfer modeling-based applications in remote sensing. These applications are even more necessary to perform high-throughput phenotyping and precision agriculture tasks. This study demonstrates an automated Python framework that can calculate the solar-sensor zenith and azimuth angles for a push-broom hyperspectral camera equipped in a UAV. First, the hyperspectral images were radiometrically and geometrically corrected. Second, the high-precision Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) data for the flight path was extracted and corresponding UAV points for each pixel were identified. Finally, the angles were calculated using spherical trigonometry and linear algebra.

(back to top)

Built With

The project is built with many open source Python packages. However, following libraries are the backbone of the project:

(back to top)

Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

For this project, anaconda is suggested. Create a virtual environment in anaconda using Python 3x.

Set up the notebooks

  1. Create a separate environment in conda.
  2. Install all the packages required using either pip or conda.
  3. Clone the repo
  4. Open the notebooks in the notebooks folder. Use the two notebooks chronologically.

Datasets

The dataset provided for testing the codes is provided in Google Drive. Please follow this link to download the dataset and then place it in the data folder of your repository. The folder contains a HSI cube and the IMU_GPS data in txt format.

(back to top)

Contributing

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

(back to top)

License

Distributed under the MIT License. See LICENSE.txt for more information.

(back to top)

Contact

(back to top)

Acknowledgments

(back to top)

Owner
Sourav Bhadra
PhD Student in GeoInformatics
Sourav Bhadra
Music Classification: Beyond Supervised Learning, Towards Real-world Applications

Music Classification: Beyond Supervised Learning, Towards Real-world Applications

104 Dec 15, 2022
Segmentation Training Pipeline

Segmentation Training Pipeline This package is a part of Musket ML framework. Reasons to use Segmentation Pipeline Segmentation Pipeline was developed

Musket ML 52 Dec 12, 2022
Generic Event Boundary Detection: A Benchmark for Event Segmentation

Generic Event Boundary Detection: A Benchmark for Event Segmentation We release our data annotation & baseline codes for detecting generic event bound

47 Nov 22, 2022
Tiny Object Detection in Aerial Images.

AI-TOD AI-TOD is a dataset for tiny object detection in aerial images. [Paper] [Dataset] Description AI-TOD comes with 700,621 object instances for ei

jwwangchn 116 Dec 30, 2022
Transfer Learning library for Deep Neural Networks.

Transfer and meta-learning in Python Each folder in this repository corresponds to a method or tool for transfer/meta-learning. xfer-ml is a standalon

Amazon 245 Dec 08, 2022
TJU Deep Learning & Neural Network

Deep_Learning & Neural_Network_Lab 实验环境 Python 3.9 Anaconda3(官网下载或清华镜像都行) PyTorch 1.10.1(安装代码如下) conda install pytorch torchvision torchaudio cudatool

St3ve Lee 1 Jan 19, 2022
Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow.

Denoised-Smoothing-TF Minimal implementation of Denoised Smoothing: A Provable Defense for Pretrained Classifiers in TensorFlow. Denoised Smoothing is

Sayak Paul 19 Dec 11, 2022
PyTorch implementation of PNASNet-5 on ImageNet

PNASNet.pytorch PyTorch implementation of PNASNet-5. Specifically, PyTorch code from this repository is adapted to completely match both my implemetat

Chenxi Liu 314 Nov 25, 2022
Koç University deep learning framework.

Knet Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU

1.4k Dec 31, 2022
Generative Models for Graph-Based Protein Design

Graph-Based Protein Design This repo contains code for Generative Models for Graph-Based Protein Design by John Ingraham, Vikas Garg, Regina Barzilay

John Ingraham 159 Dec 15, 2022
Nsdf: A mesh SDF with just some code we can directly paste into our raymarcher

nsdf Representing SDFs of arbitrary meshes has been a bit tricky so far. Express

Jan Ivanecky 5 Feb 18, 2022
Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition

Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition How Fast Compare to Other Zero-Shot NAS Proxies on CIFAR-10/100 Pre-trained Model

190 Dec 29, 2022
Pytorch reimplementation of the Mixer (MLP-Mixer: An all-MLP Architecture for Vision)

MLP-Mixer Pytorch reimplementation of Google's repository for the MLP-Mixer (Not yet updated on the master branch) that was released with the paper ML

Eunkwang Jeon 18 Dec 08, 2022
LETR: Line Segment Detection Using Transformers without Edges

LETR: Line Segment Detection Using Transformers without Edges Introduction This repository contains the official code and pretrained models for Line S

mlpc-ucsd 157 Jan 06, 2023
A mini library for Policy Gradients with Parameter-based Exploration, with reference implementation of the ClipUp optimizer from NNAISENSE.

PGPElib A mini library for Policy Gradients with Parameter-based Exploration [1] and friends. This library serves as a clean re-implementation of the

NNAISENSE 56 Jan 01, 2023
Implementation of a Transformer, but completely in Triton

Transformer in Triton (wip) Implementation of a Transformer, but completely in Triton. I'm completely new to lower-level neural net code, so this repo

Phil Wang 152 Dec 22, 2022
TensorFlow implementation of ENet, trained on the Cityscapes dataset.

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

Fredrik Gustafsson 248 Dec 16, 2022
Sequence to Sequence Models with PyTorch

Sequence to Sequence models with PyTorch This repository contains implementations of Sequence to Sequence (Seq2Seq) models in PyTorch At present it ha

Sandeep Subramanian 708 Dec 19, 2022
Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Hiroshechka Y 33 Dec 26, 2022
Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation

SSWS-loss_function_based_on_MS-TCN Supervised Sliding Window Smoothing Loss Function Based on MS-TCN for Video Segmentation Supervised Sliding Window

3 Aug 03, 2022