Introductory Digital Image Processing: A Remote Sensing Perspective
Introductory Digital Image Processing: A Remote Sensing Perspective
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
WSEAS Transactions on Computers
A method for multi-spectral image segmentation evaluation based on synthetic images
Computers & Geosciences
Ripe tomato extraction for a harvesting robotic system
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
An automatic region-based image segmentation algorithm for remote sensing applications
Environmental Modelling & Software
Object based image classification: state of the art and computational challenges
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques. The correlation image analysis is based on the fact that pairs of brightness values from the same geographic area (e.g. an object) between bi-temporal image datasets tend to be highly correlated when little change occurres, and uncorrelated when change occurs. Five different change detection methods were investigated to determine how new contextual features could improve change classification results, and if an object-based approach could improve change classification when compared with per-pixel analysis. The five methods examined include (1) object-based change classification incorporating object correlation images (OCIs), (2) object-based change classification incorporating neighbourhood correlation images (NCIs), (3) object-based change classification without contextual features, (4) per-pixel change classification incorporating NCIs, and (5) traditional per-pixel change classification using only bi-temporal image data. Two different classification algorithms (i.e. a machine-learning decision tree and nearest-neighbour) were also investigated. Comparison between the OCI and the NCI variables was evaluated. Object-based change classifications incorporating the OCIs or the NCIs produced more accurate change detection classes (Kappa approximated 90%) than other change detection results (Kappa ranged from 80 to 85%).