Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring
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RESEARCH ARTICLE
Wavelet Based Post Classification Change Detection Technique for Urban Growth Monitoring R. A. Alagu Raja & V. Anand & A. Senthil Kumar & Sandeep Maithani & V. Abhai Kumar
Received: 2 November 2011 / Accepted: 26 December 2011 / Published online: 20 March 2012 # Indian Society of Remote Sensing 2012
Abstract Urban areas are the most dynamic region on earth. Their size has been constantly increased during the past and this process will go on in the future. Since there is no standard policy and guidelines for construction of buildings and urban planning, cities tend to have irregular growth. Many cities in the world face the problem of urban sprawl in its suburbs. So issues of urban sprawl need to be settled with the help of technologies such as satellite remote sensing and
R. A. A. Raja (*) Remote Sensing & GIS Lab, Thiagarajar College of Engineering, Madurai 625015, India e-mail: [email protected] V. Anand Remote Sensing, Chennai, India e-mail: [email protected] A. S. Kumar National Remote Sensing Centre, Indian Space Research, Organisation (ISRO), Hyderabad 500 037, India e-mail: [email protected] S. Maithani HUSAG, Indian Institute of Remote Sensing, Dehradun, India e-mail: [email protected] V. A. Kumar Thiagarajar College of Engineering, Madurai 625015, India e-mail: [email protected]
automated change detection. This paper presents a wavelet based post classification change detection technique that is applied to 1996 and 2004 MSS images of Madurai City, South India to determine the urban growth. The classification stage of the technique uses coilflet wavelet filter to correlate with the MSS land cover images of Madurai city to derive texture feature vector and this feature vector is inputted to a fuzzy-c means classifier, an unsupervised classification procedure. The post classification change detection technique is employed for identifying the newly developed urban fringe of the study area. The error matrix analysis is used to assess the accuracy of the change map. The performance of the presented technique is found superior than that of classical change detection methods such as image differencing, change vector analysis and principal component analysis. Keywords Wavelet transform . Feature extraction . Classification . Change detection . Accuracy assessment
Introduction Remote Sensing technology provides powerful techniques to monitor environmental and land use/cover changes. In the past few years, there has been a growing interest in the development of change detection techniques for the analysis of multitemporal remote sensing data (Singh 1989). This interest stems from the wide range of applications in which change detection
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methods can be used, like urban planning, agricultural surveys, forest monitoring and environmental monitoring (Lindsay et al. 1996 and Niedermeier et al. 2000) etc. Change detection in remote sensing aims at identifying changes of two registered, aerial or satellite multispectral images from the same geographical area obtained at two different
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