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
Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*

Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*. The algorithm was extremely

1 Mar 28, 2022
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.

MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait

13 Dec 01, 2022
YKKDetector For Python

YKKDetector OpenCVを利用した機械学習データをもとに、VRChatのスクリーンショットなどからYKKさん(もとい「幽狐族のお姉様」)を検出できるソフトウェアです。 マニュアル こちらから実行環境のセットアップから解説する詳細なマニュアルをご覧いただけます。 ライセンス 本ソフトウェア

あんふぃとらいと 5 Dec 07, 2021
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
Code + pre-trained models for the paper Keeping Your Eye on the Ball Trajectory Attention in Video Transformers

Motionformer This is an official pytorch implementation of paper Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers. In this rep

Facebook Research 192 Dec 23, 2022
Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

Evaluating the Factual Consistency of Abstractive Text Summarization Authors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher Int

Salesforce 165 Dec 21, 2022
Implementation of the method proposed in the paper "Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation"

Neural Descriptor Fields (NDF) PyTorch implementation for training continuous 3D neural fields to represent dense correspondence across objects, and u

167 Jan 06, 2023
Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Demetri Pananos 9 Oct 04, 2022
Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

Learning multiple gaits of quadruped robot using hierarchical reinforcement learning We propose a method to learn multiple gaits of quadruped robot us

Yunho Kim 17 Dec 11, 2022
Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Outlier Exposure This repository contains the essential code for the paper Deep Anomaly Detection with Outlier Exposure (ICLR 2019). Requires Python 3

Dan Hendrycks 464 Dec 27, 2022
Official implementation of "Implicit Neural Representations with Periodic Activation Functions"

Implicit Neural Representations with Periodic Activation Functions Project Page | Paper | Data Vincent Sitzmann*, Julien N. P. Martel*, Alexander W. B

Vincent Sitzmann 1.4k Jan 06, 2023
Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL"

Sample Code for "Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL" This is the official codebase for Pessimism Meets I

3 Sep 19, 2022
ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

ManimML ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

259 Jan 04, 2023
Fine-tune pretrained Convolutional Neural Networks with PyTorch

Fine-tune pretrained Convolutional Neural Networks with PyTorch. Features Gives access to the most popular CNN architectures pretrained on ImageNet. A

Alex Parinov 694 Nov 23, 2022
Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe

Traductor de señas Traductor de lengua de señas al español basado en Python con Opencv y MedaiPipe Requerimientos 🔧 Python 3.8 o inferior para evitar

Jahaziel Hernandez Hoyos 3 Nov 12, 2022
Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network

Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network This repository is the official implementation of Speech Separati

Kai Li (李凯) 116 Nov 09, 2022
Multi-Output Gaussian Process Toolkit

Multi-Output Gaussian Process Toolkit Paper - API Documentation - Tutorials & Examples The Multi-Output Gaussian Process Toolkit is a Python toolkit f

GAMES 113 Nov 25, 2022
Code and experiments for "Deep Neural Networks for Rank Consistent Ordinal Regression based on Conditional Probabilities"

corn-ordinal-neuralnet This repository contains the orginal model code and experiment logs for the paper "Deep Neural Networks for Rank Consistent Ord

Raschka Research Group 14 Dec 27, 2022
TensorFlow implementation of the algorithm in the paper "Decoupled Low-light Image Enhancement"

Decoupled Low-light Image Enhancement Shijie Hao1,2*, Xu Han1,2, Yanrong Guo1,2 & Meng Wang1,2 1Key Laboratory of Knowledge Engineering with Big Data

17 Apr 25, 2022
Phylogeny Partners

Phylogeny-Partners Two states models Instalation You may need to install the cython, networkx, numpy, scipy package: pip install cython, networkx, num

1 Sep 19, 2022