Multiband spectro-radiometric satellite image analysis with K-means cluster algorithm

Overview

Multi-band Spectro Radiomertric Image Analysis with K-means Cluster Algorithm

Overview

Multi-band Spectro Radiomertric images are images comprising of several channels / bands which hold information on band energy in each pixel.
The most common multi band channels are the RGB (Red Green Blue) channels of the visible light spectrum.

The images used are LANDSAT 8 satellite images and each image consist of three bands, namely: Thermal Infrared, Red and Near infrared bands corresponding to band 10, band 4 and band 5 of LANDSAT 8 satellite imagery with wavelengths of 10.895µm, 0.655µm and 0.865µm respectively.

Each pixel in each bands of each image are used to compute three features namely: NDVI (Normalized Differential Vegetative Index), PV (Portion of Vegetation) and LST (Land Surface Temperature).

The K-means cluster algorithm is initialized and the "number of clusters" hyper-parameter is set to 60. The algorithm is then trained on the extracted features and forms 60 different clusters represented by each of the 60 centroids.

These centroids are stored in the "ouput" folder and will be futher studied to learn what NDVI, PV and LST combinations a geograhical location might need to have for the occurence and spread of wild fire to be highly probable.



Features

NDVI (Normalized Differential Vegetative Index):

The Normalized Differential Vegetative Index is a metric for checking the presence and health of a vegetation in a given region.
It is basically how much RED light energy from the visible light spectrum is absorbed by the plant and how much NIR (near-infrared rays) it emmits.
Healthy vegetation absorbs red-light energy to fuel photosynthesis and create chlorophyll, and a plant with more chlorophyll will reflect more near-infrared energy than an unhealthy plant.
The NDVI ranges from -1 to 1, -1 corresponds to a very unhealthy plant and 1 corresponds to a very healthy plant.

The mathematical expression for NDVI is:
NDVI = (NIR - RED) / (NIR + RED)


PV (Portion of Vegetation):

Portion of Vegetation is the ratio of the vertical projection area of vegetation on the ground to the total vegetation area

The mathematical expression for PV is:
PV = (NDVI - NDVImin) / (NDVImin + NDVImax)
NDVImin is the minimum NDVI value a pixel holds in a single image
NDVImin is the maximum NDVI value a pixel holds in a single image


LST (Land Surface Temperature):

Land Surface Temperature is the radiative temperature / intensity of the land surface

The mathematical expression for LST is:
LST = BT / ( 1 + ( ( kn * BT / p ) * np.log(E) ) )

BT is brighness Temperature in celcius and is mathematically expressed as:
BT = (K2 / np.log( ( K1 / TOA ) + 1 )) - 273.15
where K1 and K2 are landsat 8 constants 774.8853 and 1321.0789 respectively

TOA (Top of Atmosphere) Reflectance is a unitless measurement which provides the ratio of radiation reflected to the incident solar radiation on a given surface.
It is mathematically expressed as:
TOA = ML * TIR + Al
where ML and Al are landsat 8 constants 3.42E-4 and 0.1 respectively.

p is mathematically expressed as:
p = hc/A
where h, c and a are plank's constant, speed of light and boltzmann constant respectively

E is emissivity of the land surface and is mathematically expressed as:
( Ev * PV * Rv ) + ( Es * ( 1 - PV ) * Rs ) + C
where:
Ev (Vegitation Emissivity) of location = 0.986
Es (Soil Emissivity) of location = 0.973
C (topography factor) of location = 0.0001
Rv =(0.92762 + (0.07033PV))
Rs=(0.99782 + (0.05362
PV))



Dependencies

  • Rasterio
  • Numpy
  • Pandas
  • Sklearn
  • Pickle


Setup

clone the repository and download the 'requirement.txt' files, then open terminal in the working directory and type 'pip install -r requirements.txt' to install all the requirements for this project.
Owner
Chibueze Henry
A machine learning enthusiast and developer as well as a full-stack web developer
Chibueze Henry
Udacity's CS101: Intro to Computer Science - Building a Search Engine

Udacity's CS101: Intro to Computer Science - Building a Search Engine All soluti

Phillip 0 Feb 26, 2022
Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)

Skyformer This repository is the official implementation of Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr"om Method (NeurIPS 2021).

Qi Zeng 46 Sep 20, 2022
ICNet for Real-Time Semantic Segmentation on High-Resolution Images, ECCV2018

ICNet for Real-Time Semantic Segmentation on High-Resolution Images by Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, Jiaya Jia, details a

Hengshuang Zhao 594 Dec 31, 2022
PyTorch implementation of MoCo: Momentum Contrast for Unsupervised Visual Representation Learning

MoCo: Momentum Contrast for Unsupervised Visual Representation Learning This is a PyTorch implementation of the MoCo paper: @Article{he2019moco, aut

Meta Research 3.7k Jan 02, 2023
Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis

WASP2 (Currently in pre-development): Allele-specific pipeline for unbiased read mapping(WIP), QTL discovery(WIP), and allelic-imbalance analysis Requ

McVicker Lab 2 Aug 11, 2022
Spectrum Surveying: Active Radio Map Estimation with Autonomous UAVs

Spectrum Surveying: The Python code in this repository implements the simulations and plots the figures described in the paper “Spectrum Surveying: Ac

Universitetet i Agder 2 Dec 06, 2022
In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard test set accuracy

PixMix Introduction In real-world applications of machine learning, reliable and safe systems must consider measures of performance beyond standard te

Andy Zou 79 Dec 30, 2022
A PyTorch port of the Neural 3D Mesh Renderer

Neural 3D Mesh Renderer (CVPR 2018) This repo contains a PyTorch implementation of the paper Neural 3D Mesh Renderer by Hiroharu Kato, Yoshitaka Ushik

Daniilidis Group University of Pennsylvania 1k Jan 09, 2023
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

Little Ball of Fur is a graph sampling extension library for Python. Please look at the Documentation, relevant Paper, Promo video and External Resour

Benedek Rozemberczki 619 Dec 14, 2022
We propose a new method for effective shadow removal by regarding it as an exposure fusion problem.

Auto-exposure fusion for single-image shadow removal We propose a new method for effective shadow removal by regarding it as an exposure fusion proble

Qing Guo 146 Dec 31, 2022
Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations

NANSY: Unofficial Pytorch Implementation of Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations Notice Papers' D

Dongho Choi 최동호 104 Dec 23, 2022
A baseline code for VSPW

A baseline code for VSPW Preparation Download VSPW dataset The VSPW dataset with extracted frames and masks is available here.

28 Aug 22, 2022
Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Official repository of the paper Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Soubhik Sanyal 689 Dec 25, 2022
Self-Learned Video Rain Streak Removal: When Cyclic Consistency Meets Temporal Correspondence

In this paper, we address the problem of rain streaks removal in video by developing a self-learned rain streak removal method, which does not require any clean groundtruth images in the training pro

Yang Wenhan 44 Dec 06, 2022
Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection.

WOOD Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection. Abstract The training and test data for deep-neural-ne

8 Dec 24, 2022
The all new way to turn your boring vector meshes into the new fad in town; Voxels!

Voxelator The all new way to turn your boring vector meshes into the new fad in town; Voxels! Notes: I have not tested this on a rotated mesh. With fu

6 Feb 03, 2022
An addon uses SMPL's poses and global translation to drive cartoon character in Blender.

Blender addon for driving character The addon drives the cartoon character by passing SMPL's poses and global translation into model's armature in Ble

犹在镜中 153 Dec 14, 2022
Competitive Programming Club, Clinify's Official repository for CP problems hosting by club members.

Clinify-CPC_Programs This repository holds the record of the competitive programming club where the competitive coding aspirants are thriving hard and

Clinify Open Sauce 4 Aug 22, 2022
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR)

Ilya Kostrikov 3k Dec 31, 2022
[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”,

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in

VITA 24 Dec 17, 2022