Unsupervised Multiresolution Segmentation for Images with Low Depth of Field
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Computer and Robot Vision
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution-based watersheds for efficient image segmentation
Pattern Recognition Letters
Image segmentation using evolutionary computation
IEEE Transactions on Evolutionary Computation
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Image segmentation is a decisive and fundamental step for remote sensing information retrieval and classification. High-resolution satellite image classification using standard per-pixel approaches is difficult because of the high volume of data, as well as high spatial variability within the objects. One approach to deal with this problem is to reduce the image complexity by dividing it into homogenous segments prior to classification. This has the added advantage that segments can not only be classified on basis of spectral information but on a host of other features such as neighborhood, size, texture and so forth. Segmentation of the images is carried out using the region based algorithms such as marker-based watershed transform by taking the advantage of multi-resolution and multi-scale gradient algorithms. This paper presents an efficient method for image segmentation based on a multi-resolution application of a wavelet transform and marker-based watershed segmentation algorithm. Experimental result of proposed technique gives promising result on QuickBird images. It can be applied to the segmentation of noisy or degraded images as well as reduce over-segmentation.