Last updated 2/2021
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.00 GB | Duration: 6h 24m
Land Use Scratch to Advance, All Softwares of Remote Sensing and GIS. Machine Learning, 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
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