Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation

By M. Baatz, A. Schäpe. 2000

2,714 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/364cc1ff514a2e11d21a101dc072575e5487d17e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data (2010, methodology): “Parameters (2), (5), and (6) are used as implemented in the multiresolution segmentation and described by Baatz and Scha¨pe (2000) and Benz et al .”
Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images (2007, unknown): “Image segmentation into histological objects is a multistage process based on an object-oriented multiresolution image segmentation technique [23].”
Geographic Object-Based Image Analysis – Towards a new paradigm (2014, unknown): “In several of these approaches (see e.g. Baatz and Schäpe, 2000; Lang et al., 2010; Tiede et al., 2010a; Tiede et al., 2010b, 2011, 2012) segmentations are used very ﬂexibly in initial stages and are also tailored in a later stage for speciﬁc classes or regions in the image when the classiﬁcation…”
ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data (2019, methodology): “Popular image segmentation algorithm in remote sensing include watershed segmentation (Vincent and Soille, 1991), multi-resolution segmentation (Baatz and Sch¨ape, 2000) and mean-shift segmentation (Comaniciu and Meer, 2002).”
An Object-oriented Approach to Urban Forest Mapping in Phoenix (2007, methodology): “To more accurately estimate real world objects, the image is apportioned into basic units for analysis at the object-appropriate scale before classification can occur (Baatz and Shäpe, 2000; Blaschke et al., 2000).”
Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands (2013, methodology): “One of its strength is the ability to combine spectral information and spatial information for extracting target objects [36,38].”
Improved landsat-based forest mapping in steep mountainous terrain (2003, methodology): “The segmentation process in the used software eCognition (Definiens-Imaging, 2001) is a bottom up region merging technique starting with randomly selected one-pixel objects, where a binary counter guarantees a regular spatial distribution and simultaneous growth of treated objects (Baatz and Schäpe,”
A generic procedure for semantics-oriented landform classification using object-based image analysis (2011, methodology): “MRS iteratively performs pair-wise clustering of neighboring objects (respectively cells at the initial level) based on the two optimization principles of minimizing internal heterogeneity of objects while maximizing their external heterogeneity [11].”
Object based image analysis for remote sensing (2010, methodology): “The eCognition software built on to the approach originally known as Fractal Net Evolution (Baatz and Schäpe, 2000) and developed into completely programmable workflows (Baatz et al.”
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability (2008, methodology): “But segmentation does not automatically lead to meaningful objects in the real world (Gorte 1998, Molenaar and Cheng 1998, Baatz and Schäpe 2000) but to ‘relatively homogeneous areas’.”