Unlike traditional pixel-based classication methods, segment-based classication is an approach that classies a remotely-sensed image based on image segments. Segmentation is the process o dening homogeneou s pixels into these spec trally similar image segments. Te goal o the se gmentation process is to change the characteristics o the image into more meaningul ones, t hus acilitating interpretation and classication . Because these image segments better represent objects in the landscape than do the original pixels, each step o the classication process, rom dening training sites to classiying rom these segments, is simplied. I t is also possible to achieve better accuracy. Te common salt-and-pepper eect that results rom a pixel-based classication is reduced and a more cartographic-grade map is the result. Segment-based classication is highly suited or applications that utilize medium to high resolution satellite imagery and is a useul addition or those mapping land cover and monitoring land change. Other applications such as biodiversity and habitat mapping, where a lone pixel in the classication result may not t in its context, can also take advantage o t his classication appr oach. Tis paper explores how this unctionality is incorporated within IDRISI and also outlines the workfow. Utilizing a powerul set o existing classication tools, rom the traditional maximum likelihood to the cutting edge machine learning tools such as the multi-layer perceptro n and classication tree analysis, IDRISI’ s methodology combines pixel-based and segment-based approaches . Tree modules have been developed to acilitate the creation o a segment-based classied map. SEGMENAION creates an image o segments. SEGRAIN interactively develops training sites and signatures. SEGCLASS classies the image utilizing a majority rule algorithm. image segmentation Te SEGMENAION module generates an image o segments where pixels identied within a segment share a homogeneous spectral similarit y. Across space and over all input bands, a moving window assesses this similarity and segments are dened according to a user- specied similarity threshold. Te smaller the threshold, the more homogeneou s the segments. A larger threshold will cause a more heterogeneous and generalized segmentation result. IDRISI utilizes a watershed delineation approach or the initial p artition o the input images. Ten, similar segments are merged to orm larger segments, based on a user-specied similarity thr eshold. Te segmentation process consists o three procedures: 1. Derive surace image First, or each input image, a correspondin g variance image is cr eated using a user -dened lter . As an example, pixels that are more homogeneous will be assigned lower variance values, while pixels at the boundaries o homogeneou s regions will be assigned higher values. I there is more than one input image, the nal surace image will be a weighted average o all variance images r om all image layers . Te weight o each image is specied by the user . 2. Delineate watersheds Te pixels’ values within the variance image are then treated like elevation values within a digital elevation model. Pixels are grouped into one watershed i they are within one catchment. Each watershed is given a unique integral value as its ID. 3. Merge watersheds Te watersheds or image segments are t hen merged to orm new segments. Te process is guided by the ollowing logic: Segmentation and Segment-Based Classifcation IDRISI Focus Paper Copyright 2009 Clark Labs The SEGMENTATION module creates an image o segments that have spectral similarity across many input bands. This example shows two levels o segmentation rom hal-meter 4-band aerial photography in the blue, green, red and near inrared wavelengths or Woburn, Massachusetts in 2005. The image on the let uses a larger similarity threshold than the one on the right, resulting in more generalized, less homogeneous segments. Using this threshold, the image allows or segments that wholly contain building eatures.