Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis

• Published in: IEEE transactions on geoscience and remote sensing 2011-12, Vol.49 (12), p.4928-4943
• Main Authors: Martha, T. R., Kerle, N., van Westen, C. J., Jetten, V., Kumar, K. V.
• Format: Article
• Language: English
• Subjects: Applied geophysics; Disaster support; Earth sciences; Earth, ocean, space; Exact sciences and technology; Feature extraction; Image color analysis; Image segmentation; India; Internal geophysics; K -means cluster analysis; object-oriented analysis (OOA); Optimization; segmentation; Terrain factors
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2011.2151866

To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.