Srbija Posted August 4, 2022 Share #1 Posted August 4, 2022 Land Use Land Cover Classification Gis, Erdas, Arcgis, Envi 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 Hidden Content Give reaction to this post to see the hidden content. 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