Land use Land cover classification GIS, ERDAS, ArcGIS, ML

Learn when to use supervised, unsupervised, and combined land use and land cover classification methods in GIS to analyze satellite imagery for urban, agriculture land, and barren land areas.

Derive and refine land use signatures for agriculture, urban, water, and other classes by collecting diverse samples from Google Earth imagery, and crosschecking to improve classification accuracy.

Create a folder named signature, save your signature file, and use it to run the land use land cover classification analysis in GIS tools.

Apply unsupervised classification for land use and land cover in GIS using erdas and ArcGIS, noting pixel-based clustering, class iteration, and common accuracy limitations.

Explore a combined unsupervised classification method without a signature file for land use in GIS tools, linking classes to real labels, with iterative processing and relinking to improve accuracy.

Identify water, urban areas, agricultural land, and forest in a land-use map using GIS; apply pixel selection, color coding, and iterative corrections to improve accuracy.

Demonstrate creating a new land-use class in the same file by drawing polygons to convert urban areas to a riverbed, with color-table updates and pixel-level corrections for land-use classification.

Calculate the area of land-use classes in a GIS classified image by using the field calculator in ERDAS/ArcGIS to convert pixel counts to square kilometers based on pixel size.

Create land use maps in ENVI by loading an image, performing supervised classification with training samples, and exporting the labeled land cover results.

Open the classified land-use image in Erdas and create random sample points. Verify each point against the reference and generate an accuracy report showing 94.23 percent.

Apply a statistical median filter to a land use image to remove small errors, overwrite pixels, back up data, compare before and after, and save the enhanced image.