This document discusses two main techniques for digital image classification in remote sensing: unsupervised and supervised classification. Unsupervised classification groups pixels into clusters based on reflectance properties and the user assigns land cover classes. Supervised classification involves selecting training sites for each land cover class, which are used to classify the image based on spectral signatures. Both techniques aim to categorize land cover features from remote sensing imagery.

1. DIGITAL IMAGE CLASSIFICATION TECHNIQUES
Digital Image Classification Techniques group pixels to represent land cover
features. Land cover could be forested, urban, agricultural and other types of
features. There are three main image classification techniques.
IMAGE CLASSIFICATION TECHNIQUES IN REMOTE SENSING:
 Unsupervised image classification
 Supervised image classification
UNSUPERVISED IMAGE CLASSIFICATION
In this, the Pixels are grouped based on the reflectance properties of pixels.
These groupings are called “clusters.”
In this classification technique the user manually identifies each cluster with land
cover classes. It’s often the case that multiple clusters represent a single land
cover class. The user merges clusters into a land cover type. The unsupervised
classification image classification technique is commonly used when no sample
sites exist.
Unsupervised Classification Steps:
 Generate clusters
 Assign classes

2. Unsupervised Classification Diagram
SUPERVISED CLASSIFICATION
The user selects representative samples for each land cover class in the digital
image. These sample land cover classes are called “training sites”.
The classification of land cover is based on the spectral signature defined in the
training set. The digital image classification software determines each class on
what it resembles most in the training set. The common supervised classification
algorithms are maximum likelihood and minimum-distance classification.
Supervised Classification Steps:
 Select training areas
 Generate signature file
 Classify

3. Supervised Classification Diagram
REFERENCES
1. Blaschke T, 2010. Object based image analysis for remote sensing. ISPRS
Journal of Photogrammetry and Remote Sensing 65 (2010) 2–16
2. Object-Based Classification vs Pixel-Based Classification: Comparitive
Importance of Multi-Resolution Imagery (Robert C. Weih, Jr. and Norman
D. Riggan, Jr.)
3. Multiresolution Segmentation: an optimization approachfor high quality
multi-scale image segmentation (Martin Baatz & Arno Schape)