A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

Remote Sensing

Reza Shah-Hosseini¹  Saeid Homayouni³  Abdolreza Safari¹  Gonzalo Pajares Martinsanz²  Parth Sarathi Roy² 

[1] University College of Engineering, School of Surveying and Geospatial Engineering, University of Tehran, Tehran 11155-4563, Iran; E-Mails:University College of Engineering, School of Surveying and Geospatial Engineering, University of Tehran, Tehran 11155-4563, Iran;;Department of Geography, University of Ottawa, Ottawa, ON K1N 6N5, Canada; E-Mail:

关键词: kernel-based CD;    similarity space;    Hilbert space;    remotely sensed data;    clustering;    One-class classifiers;   

DOI  :  10.3390/rs71012829

来源: mdpi

【 摘 要 】

Detection of damages caused by natural disasters is a delicate and difficult task due to the time constraints imposed by emergency situations. Therefore, an automatic Change Detection (CD) algorithm, with less user interaction, is always very interesting and helpful. So far, there is no existing CD approach that is optimal and applicable in the case of (a) labeled samples not existing in the study area; (b) multi-temporal images being corrupted by either noise or non-normalized radiometric differences; (c) difference images having overlapped change and no-change classes that are non-linearly separable from each other. Also, a low degree of automation is not optimal for real-time CD applications and also one-dimensional representations of classical CD methods hide the useful information in multi-temporal images. In order to resolve these problems, two automatic kernel-based CD algorithms (KCD) were proposed based on kernel clustering and support vector data description (SVDD) algorithms in high dimensional Hilbert space. In this paper (a( a new similarity space was proposed in order to increase the separation between change and no-change classes, and also to decrease the processing time, (b) three kernel-based approaches were proposed for transferring the multi-temporal images from spectral space into high dimensional Hilbert space, (c) automatic approach was proposed to extract the precise labeled samples; (d) kernel parameter was selected automatically by optimizing an improved cost function and (e) initial value of the kernel parameter was estimated by a statistical method based on the L2-norm distance. Two different datasets including Quickbird and Landsat TM/ETM+ imageries were used for the accuracy of analysis of proposed methods. The comparative analysis showed the accuracy improvements of kernel clustering based CD and SVDD based CD methods with respect to the conventional CD techniques such as Minimum Noise Fraction, Independent Component Analysis, Spectral Angle Mapper, Simple Image differencing and Image Rationing, and also the computational cost analysis showed that implementation of the proposed CD method in similarity space decreases the processing runtime.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.