Differences pixel-wise, sub-pixel-wise & object based classifications by Dyana Hassan https://padlet.com/ladydyana87/oub3n03exwgr What do you understand? Please share here! :) en-us 2016-10-11 08:30:38 UTC 2023-04-19 02:39:45 UTC hello@padlet.com https://padlet-assets.s3.amazonaws.com/icons/Cornershop.png Nur Amalina Bt Mohd Ropi https://padlet.com/ladydyana87/oub3n03exwgr/wish/129991779 2016-10-11 23:08:41 UTC https://padlet.com/ladydyana87/oub3n03exwgr/wish/129991779 Camalia Saini binti Hamsa https://padlet.com/ladydyana87/oub3n03exwgr/wish/129994412 Hard classification

Definition: It produces hard, discrete categories of land classification such as land, sea. It can be further divide to supervised and unsupervised classification. 

Supervised Classification:  The identity and location of the land cover types is known priori. This area is known as traning sites, because it will be used to train the classification algorithm for the rest of land cover mapping.

Example of Supervised Classification Algorithm : Parallelpiped, minimum distance, maximum likelihood

Unsupervised Classification: The identity and location of the land cover types is not known. Computer will group the pixels according similar spectral characteristic and analyst will relabelled it according to the classes.

Example of Unsupervised Classification: Chain Method, ISODATA

Soft classification

Definition: It produces fuzzy classification that is based in the percent value of the categories found within the pixel (eg: 10% water, 20% vegetation, 70%bare land).

Example of Soft Classification Algorithm: Fuzzy classification, Neural Network

Object Oriented Classification

Definition: It segmented the image into homogenous objects by taking account of it forms, texture, and spectral information. 

Example of Object Oriented Classification : Fractal Net Evolution Approach  (in the Definiens Developer)

]]> 2016-10-11 23:32:56 UTC https://padlet.com/ladydyana87/oub3n03exwgr/wish/129994412 NRAH https://padlet.com/ladydyana87/oub3n03exwgr/wish/130310410 Image Classification Method

Pixel-wise image classification
- classic image classification technique.
- each pixel is pure and labeled as single land use land cover.
- two types of pixel-wise : supervised and unsupervised classification.
- supervised : user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image.
- unsupervised : based on the software analysis of an image without the user providing sample classes.

Sub-pixel-wise image classification
- exploits (only) the spectral information of the image where it gives information about different classes within a pixel.

Object-based image classification
- identification of image objects, or segments, that are spatially contiguous pixels of similar texture, color, and tone.
- ore effective than pixel-based methods when classifying high-resolution imagery.

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