Object‐based change detection using correlation image analysis and image segmentation

Abstract

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) objectbased 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 nearestneighbour) 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%).

Cultural Heritage Sites in Danger—Towards Automatic Damage Detection from Space

“…Region-based or grid-based comparison are among other approaches proposed to solve the aforementioned problems [17,18]. For efficient and fast results, Robust Difference (RD) has been initially proposed by Castilla et al [19] and successfully applied to compare DSMs [20].…”

Change Detection Algorithm for the Production of Land Cover Change Maps over the European Union Countries

“…Reference data can be expressed as points or as objects. There are a number of studies that uses point data (e.g., [30,31]). However, it has been argued that the accuracy of the object-based change detection assessment requires new methods [32] and that the validation concepts should also take into account a spatial assessment of the object boundaries [33] in addition to a simple thematic assessment.…”

Information Extraction of High-Resolution Remotely Sensed Image Based on Multiresolution Segmentation

“…Winhauck used this method and the traditional visual interpretation method to process SPOT data respectively, and the results showed that the classification accuracy is better than the latter [1]. Hofmann improved the classification accuracy of the residential areas in IKONOS images by combining spectrum of the object, texture and shape with the background information [2][3][4][5][6]. Benz considers that object-oriented extraction method can improve the efficiency of automatic extraction and has development potential in high-resolution remotely sensed images [7][8][9][10].…”