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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.
This Course Includes
udemy
4.6 (601 reviews )
6h 44m 
english
Online - Self Paced
professional certificate
Udemy
About Land use Land cover classification GIS, ERDAS, ArcGIS, ML
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 10.8
_90% practical 10% theory_
Problem faced During classification:
Some of us faced problem during classification as:
1.
Urban area and barren land has the same signature
2.
Dry river reflect the same signature as an urban area and barren land
3.
if you try to correct urban and get an error in barren
4.
In Hilly area you cannot classify forest which is in the hill shade area.
5.
Add new class after final work
How to get rid of this all problems Join this course.
What You Will 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 Show moreShow less.