Anarchic Urban Expansion Detection and Monitoring with Integration of Expert Knowledge

With the advent of very high spatial, spectral, and temporal resolution satellites, Satellite Image Time Series (SITS) analysis and interpretation become even more challenging than before. Besides, several conventional techniques for controlling and monitoring anarchic urban expansion have been init...

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Bibliographic Details
Main Authors: Chaabane, Ferdaous, Rejichi, Safa, Kefi, Chayma, Ismail, Haythem, Tupin, Florence
Format: Conference Proceeding
Language:English
Subjects:
concepts
etc
MGK
ontology
SITIS monitoring
SOTAG
SVM classification
VHR images
Online Access:Request full text
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Summary:With the advent of very high spatial, spectral, and temporal resolution satellites, Satellite Image Time Series (SITS) analysis and interpretation become even more challenging than before. Besides, several conventional techniques for controlling and monitoring anarchic urban expansion have been initiated but they remain not sufficient to overcome this issue.This paper proposes an automatic method of detection and monitoring of anarchic urban expansions starting from multi-sources and multi-temporal data (VHR satellite images and geographic information data). First, the illegal urban areas are extracted using an original SVM based technique integrating expert knowledge and auxiliary data by means of ontology construction. This leads to the formalization of the expert semantic information and the urban construction rules (often in sentences form) and their confrontation with the classification results.Secondly, the SITS classified images are modeled using Spatial-Object Temporal Adjacency Graphs (SOTAG) constructed for each region of the first image. These graphs are then classified using a Marginalized Graph Kernel (MGK) SVM based classification in order to extract regions with similar temporal evolution. We are focusing mainly on monitoring legal and illegal urban expansions. The resulted spatio-temporal map describes urban areas types and their changes over time.
ISSN:2379-190X
DOI:10.1109/ICASSP.2019.8683526