A Novel Automatic Change Detection Method for Urban High-Resolution Remotely Sensed Imagery Based on Multiindex Scene Representation

• Published in: IEEE transactions on geoscience and remote sensing Vol. 54; no. 1; pp. 609 - 625
• Main Authors: Dawei Wen, Xin Huang, Liangpei Zhang, Benediktsson, Jon Atli
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automation; Building detection; Buildings; Change detection; Construction; Feature extraction; high resolution; Histograms; Image detection; Indexes; Methods; morphological; Morphology; Remote sensing; Representations; Soil; Training; Vegetation; Vegetation mapping; ISEW, Malaysia; ISEW, China, People's Rep., Guangdong Prov., Shenzhen; China, People's Rep; change detection; morphological; Building detection; high resolution
• DOI: 10.1109/TGRS.2015.2463075
• ISSN: 0196-2892

The new generation of Earth observation sensors with high spatial resolution can provide detailed information for change detection. The widely used methods for high-resolution image change detection rely on textural/structural features. However, these spatial features always produce high-dimensional data space since they are related to a series of parameters, e.g., window sizes and directions. Machine learning methods are also commonly employed, but their performances are subject to the quantity and quality of the training samples, and hence, much effort should be made to collect the high-quality samples. To address these problems, in this study, a novel multiindex automatic change detection method is proposed for the high-resolution imagery. The notable advantages of the proposed model include the following: 1) Complicated urban scenes are represented by a set of low dimensional but semantic information indexes, replacing the high-dimensional but low-level features (e.g., textural and structural features), and 2) the change detection model is carried out automatically without using training samples since the information indexes can directly indicate the primitive urban classes. The multiindex representation refers to the enhanced vegetation index, the water index, and the recently developed morphological building index. Experiments were conducted on the multitemporal WorldView-2 images over Shenzhen City (south of China) and Kuala Lumpur (the capital of Malaysia), where promising results were achieved by the proposed method. Moreover, the traditional methods based on the state-of-the-art textural/morphological features were also implemented for the purpose of comparison, which further validates the advantages of our proposed model.