Land Use Land Cover Classification Gis, Erdas, Arcgis, Ml

Machine Learning, Land Use Scratch to Advance, All Softwares of Remote Sensing and GIS, GIS Tasks in Easy way learning.

What you'll learn

Able to do a Prefect Land use classification of Earth using satellite image

Also learn image Processing and analysis in depth

Landuse change Detection

Understand Features identification on Earth using Landsat Image

Post Landuse Pixel level corrections

Accuracy Assessment Report

Downloading of best satellite image and process

Understanding FCC satellite image and bands

Pixel level correction in land use at specific area and statistical filters

Calculate area from Pixels

Generate new class after final landuse

Learn all best method of classification.

How to achieve maximum accuracy of classification

Cut Study Area

Classify with Machine Learning

Support Vector Machine

Random Forest

Requirements

You must have ArcGIS and ERDAS or ENVI

You must have basic knowledge of GIS

Description

This is the first landuse landcover course on Udemy the most demanding topic in GIS, In this course, I covered from data download to final results. I used ERDAS, ArcGIS, ENVI and MACHINE LEARNING. I explained all the possible methods of land use classification. More then landuse, Pre-Procession of images are covered after download and after classification, how to correct error pixels are also covered, So after learning here you no need to ask anyone about lanudse classification. I explained the theoretical concept also during the processing of data. I have covered supervised, unsupervised, combined method, pixel correction methods etc. I have also shown to correct area-specific pixels to achieve maximum accuracy. Most of this course is focused on Erdas and ArcGIS for image classification and calculations. For in-depth of all methods enrol in this course.  Image classification with Machine learning also covered in this course. This course also includes an accuracy assessment report generation in erdas. Note: Each Land Use method  Section covers different Method from the beginning, So before starting landuse watch the entire course. Then start land use with a method that you think easy for you and best fit for your study area., then you will be able to it best. Different method is applicable to a different type of study area. This course is applicable to Erdas Version 2014, 2015, 2016 and 2018. and ArcGIS Version 10.1 and above, i.e 10.4, 10.7 or 10.890% practical 10% theoryProblem faced During classification:Some of us faced problem during classification as:Urban area and barren land has the same signatureDry river reflect the same signature as an urban area and barren landif you try to correct urban and get an error in barrenIn Hilly area you cannot classify forest which is in the hill shade area. Add new class after final workHow to get rid of this all problems Join this course.

Overview

Section 1: Downloading and Data Processing

Lecture 1 Downloading of Latest Satellite Images

Lecture 2 About Rating

Lecture 3 Processing of Image in ArcGIS With Metafile

Lecture 4 Image processing from Bands ArcGIS

Lecture 5 Image Processing in Erdas

Lecture 6 Image Enhancement

Lecture 7 Removing black pixels

Section 2: Understanding Satellite image and Google Earth Pro

Lecture 8 Why We Need Google Earth

Lecture 9 Downloading and Installing Google Earth Pro

Lecture 10 Erdas 2018 - Bug fix for Google Earth Pro

Lecture 11 More image improvement for better identification

Lecture 12 Linking Satellite image with pro and Investigation - Don't Skip this Video

Section 3: Which method to use and Why

Lecture 13 Understanding Methods of Land Use and When to use which method.

Section 4: Supervised Classification

Lecture 14 Signature derivation - 1

Lecture 15 Signature derivation -2

Lecture 16 Signature save

Lecture 17 Supervised classification and understand Errors

Lecture 18 Class Value corrections

Section 5: Unsupervised classification

Lecture 19 Unsupervised classification

Section 6: Combined classification

Lecture 20 pixel Brakeout

Lecture 21 Class Identification 1

Lecture 22 Class Identification 2

Lecture 23 Class information collection and arrange

Lecture 24 Re-Code

Section 7: Error pixel correction and New Class Generation

Lecture 25 Pixel corrections of landuse class

Lecture 26 New Class generation after landuse in same file

Section 8: Results from Landuse

Lecture 27 Calculate Area of Landuse classes

Lecture 28 Performing Change Detection of time series land use

Lecture 29 Making Change Detection Matrix in Excel from land use Data

Section 9: Best Practical- Landuse Task in ArcGIS and ENVI

Lecture 30 Landuse in ArcGIS

Lecture 31 Live Landuse in ENVI

Section 10: Miscellaneous

Lecture 32 Accuracy assessment in Erdas

Lecture 33 Thematic error Correction for Land Change Analysis

Lecture 34 Statistical Filters to enhance final land use image

Section 11: Miscellaneous Task - Cut Your Study Area

Lecture 35 Cut Study Area in Erdas

Lecture 36 Cut Study Area in ArcGIS

Section 12: Download Data used in Course

Lecture 37 Download Files of Course

Section 13: Error Resolving

Lecture 38 Google Earth Tab Not Visible in Erdas 18

Section 14: Machine Learning in ArcGIS for Image classification

Lecture 39 Introduction

Lecture 40 Downloading High Resolution Image

Lecture 41 Processing of 10 meter Resolution Image

Lecture 42 Installing Support Vector Mechanism Model

Lecture 43 Creating Training Samples to Train Model

Lecture 44 Classifying with SVM

Lecture 45 Classifying with SVM -2 More tools

Lecture 46 Classify with Random Forest Model

Lecture 47 Conclusion

Section 15: Bonus

Lecture 48 Bonus Lecture

Civil Engineers,Water Resource Experts,Master Student of GIS,PhD Students of Satellite Data Analysis,Research Scholars,GIS Analyst,Environment and Earth Science Persons,Urban and city Planner