Syam Kakarla

Data Science | Remote Sensing

Satellite Imagery Meets Computer Vision: A Tutorial for wildfire detection in Australia using Python

Image for post
Image for post
Photo by Michael Held on Unsplash

Over the past decade, Mother Earth has been harmed by unprecedented wildfires. A wildfire can be described as an uncontrollable fire that is spreading across the Wildland, Forests, Grassland, e.t.c. Wildfires have a huge impact on Humans, Nature, and the Economy and these are the major cause of greenhouse gas emissions.

The Majority of the wildfires are caused by humans practices such as agriculture burning. The below animation shows the wildfire data in 2019 across the world collected by NASA’S Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. …


Remote Sensing | Data Analysis | Python

A detailed explanation of Data Analysis on Sundarbans Satellite Imagery using Python

Image for post
Image for post
Photo by USGS on Unsplash

This article helps readers to better understand the satellite data and different methods to explore and analyze the Sundarbans satellite data using Python.

Table of Contents

  1. Introduction to Remote Sensing
  2. Sundarbans Satellite Imagery
  3. Data Analysis
  4. Vegetation and Soil Indices
  5. Water Indices
  6. Geology Indices
  7. Conclusion
  8. References

Let’s get started ✨

Introduction to Remote Sensing

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring it's reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers “sense” things about the Earth.

Electromagnetic energy, produced by the vibration of charged particles, travels in the form of waves through the atmosphere and the vacuum of space. These waves have different wavelengths and frequencies, a shorter wavelength means a higher frequency. Some like radio, microwave, and infrared waves have a longer wavelength. While others such as ultraviolet, x-rays, and gamma rays have a much shorter wavelength. Visible light sits in the middle of that range of long to shortwave radiation. This small portion of the energy is all that the human eye can detect. Instrumentation is needed to detect all other forms of electromagnetic energy. With the help of different satellites, we utilize the full range of the spectrum to explore and understand processes occurring here on Earth and other planetary bodies. …


Machine Learning | Remote Sensing

An unsupervised learning approach for Sundarbans satellite Imagery Analysis using Python

Image for post
Image for post
Photo by ImaginEarth La Terre En Images on Unsplash

This article helps readers to better understand the Sundarbans satellite data and to perform dimensionality reduction and clustering with Python.

Table of Contents

  1. Introduction to Remote Sensing
  2. Sundarbans Satellite Imagery
  3. Dimensionality Reduction
  4. k-Means Clustering
  5. Conclusion
  6. References

Let’s Get Started✨

Introduction to Remote Sensing

Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring it's reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers “sense” things about the Earth.

Electromagnetic energy, produced by the vibration of charged particles, travels in the form of waves through the atmosphere and the vacuum of space. These waves have different wavelengths and frequencies, a shorter wavelength means a higher frequency. Some like radio, microwave, and infrared waves have a longer wavelength. While others such as ultraviolet, x-rays, and gamma rays have a much shorter wavelength. Visible light sits in the middle of that range of long to shortwave radiation. This small portion of the energy is all that the human eye can detect. Instrumentation is needed to detect all other forms of electromagnetic energy. With the help of different satellites, we utilize the full range of the spectrum to explore and understand processes occurring here on Earth and other planetary bodies. …


Deep Learning | Remote Sensing

A Python hands-on tutorial on Land Cover Classification of Satellite Imagery using Convolutional Neural Networks.

Image for post
Image for post
Photo by USGS on Unsplash

Land cover classification using remote sensing data is the task of classifying pixels or objects whose spectral characteristics are similar and allocating them to the designated classification classes, such as forests, grasslands, wetlands, barren lands, cultivated lands, and built-up areas. Various techniques have been applied to land cover classification, including traditional statistical algorithms and recent machine learning approaches, such as random forest and support vector machines, e.t.c.

This article covers a hands-on Python tutorial on the land cover classification of satellite imagery using Convolutional Neural Network (CNN).

Table of Contents

  1. Introduction
  2. Convolutional Neural Networks (CNN)
  3. Salinas HSI
  4. Implementation of CNN
  5. Training
  6. Results
  7. Conclusion
  8. References


Deep Learning | Python HandsOn

A walkthrough on utilizing AutoEncoders for land cover classification of Hyperspectral Images using Python.

Image for post
Image for post
Photo by USGS on Unsplash

Dimensionality reduction has become an important aspect of machine learning. It is often considered as a preprocessing step in the machine learning problems such as classification and clustering. The presence of a large number of features in data sets affects the predictive capabilities of the classifiers. The feature extraction algorithms reduce the dimensionality of the data set, thereby paving way for the classifiers to generate comprehensive models at a reduced computational cost.

This article helps readers to understand the role of AutoEncoders in Dimensionality Reduction of Hyperspectral Images and also provides a hands-on tutorial of the implementation.

Table of Contents

  1. Introduction
  2. AutoEncoders
  3. Implementation of…


Hands-on Tutorials, Deep Learning

Using Deep Learning (DL) for land cover classification of Hyperspectral Imagery.

Image for post
Image for post
Photo by USGS on Unsplash

Table of Contents

  1. Introduction to Hyperspectral Images (HSI)
  2. Pavia University HSI
  3. Deep Neural Networks
  4. Implementation of Deep Neural Networks
  5. Training Deep Neural Network
  6. Results
  7. Conclusion
  8. References

Introduction to Hyperspectral Images (HSI)

Hyperspectral Imaging is an important technique in remote sensing, which collects the electromagnetic spectrum ranging from the visible to the near-infrared wavelength. Hyperspectral imaging sensors often provide hundreds of narrow spectral bands from the same area on the surface of the earth. In hyperspectral images (HSI), each pixel can be regarded as a high-dimensional vector whose entries correspond to the spectral reflectance in a specific wavelength.

With the advantage of distinguishing subtle spectral differences, HSIs have been widely applied in diverse areas such as Crop Analysis, Geological Mapping, Mineral Exploration, Defence Research, Urban Investigation, Military Surveillance, Flood Tracking, etc. …


Machine Learning & Data Science

Five opensource books you need to read to improve your skills in Machine Learning and Data Science

Image for post
Image for post
Photo by Jaredd Craig on Unsplash

In this article, you will get to know about 5 open source books that you must read to start your career or to improve your skills in Data Science and Machine Learning.

The annual Stack Overflow survey provides comprehensive information with the representation from a great diversity of programmers and developers across the globe, with this year’s poll being taken by nearly65,000 people. This year’s survey details which languages developers enjoy using, which are associated with the best-paid jobs, which are most commonly used, as well as developers’ preferred frameworks, databases, and integrated development environments.

We have not seen a technology that largely grows so fast ever, in the history of Stack Overflow .


PySpark Tutorial

Chapter 1: Introduction to PySpark using US Stock Price Data

Image for post
Image for post
Photo by Luke Chesser on Unsplash

PySpark is an API of Apache Spark which is an open-source, distributed processing system used for big data processing which was originally developed in Scala programming language at UC Berkely. The Spark has development APIs in Scala, Java, Python, and R, and supports code reuse across multiple workloads — batch processing, interactive queries, real-time analytics, machine learning, and graph processing. It utilizes in-memory caching, and optimized query execution for fast analytic queries against data of any size. …


Intermediate Guide

In this article, we are going to use EarthPy to handle the satellite imagery and to do Exploratory Data Analysis(EDA) effectively

Image for post
Image for post
Landsat Image by Jesse Allen and Robert Simmon, using data from the USGS Earth Explorer.

Table of Contents

  • Introduction to Satellite Imagery
  • Installation
  • How to Download Satellite Images
  • Exploratory Data Analysis(EDA) on Satellite Images
  • Final Thoughts
  • References

Let’s Get Started ✨

Introduction to Satellite Imagery

Satellite imagery has a wide range of applications which is incorporated in every aspect of human life. Especially remote sensing has evolved over the years to solve a lot of problems in different areas. In Remote Sensing, Hyperspectral remote sensors are widely used for monitoring the earth’s surface with the high spectral resolution.

Hyperspectral Image(HSI) data often contains hundreds of spectral bands over the same spatial area which provide valuable information to identify the various materials. …


Beginner’s Guide

A walkthrough on the classification of Hyperspectral Images (HSI) using python.

Image for post
Image for post
Photo by USGS on Unsplash

This article provides detailed implementation of different classification algorithms on Hyperspectral Images(HSI).

Table of Contents

  • Introduction to Hyperspectral Images(HSI)
  • Dimensionality Reduction(DR)
  • Classification Algorithms
  • Implementation — Classification on HSI
  • References

Introduction to Hyperspectral Images(HSI)

In Remote Sensing, Hyperspectral remote sensors are widely used for monitoring the earth’s surface with the high spectral resolution. Generally, the HSI contains more than three bands compared to conventional RGB Images. The Hyperspectral Images(HSI) are used to address a variety of problems in diverse areas such as Crop Analysis, Geological Mapping, Mineral Exploration, Defence Research, Urban Investigation, Military Surveillance, etc.

Use the below article which provides information on Data Collection, Data Preprocessing, and Exploratory Data Analysis on HSI. …

About

Syam Kakarla

Machine Learning Practitioner and Data Science Enthusiast, https://www.linkedin.com/in/syam-kakarla/

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store