The 30th Canadian Symposium on Remote Sening June 22-25, 2009, Lethbridge, Alberta, canada
Intelligent Systems for Remote Sensing Image Processing and Classification – a Review and Identification of Requirements Mohammad Mostafa Kamal 1, Peter J Passmore 2, Ifan D H Shepherd 3 1
Department of Geography, University of Saskatchewan, 117 Science Place, Saskatoon, SK, S7N 5C8 Canada (e-mail:
[email protected]) 2 School of Engineering and Information Sciences, Middlesex University, The Burroughs, Hendon, London, NW4 4BT, UK (e-mail:
[email protected]) 3 Business School, Middlesex University, The Burroughs, Hendon, London, NW4 4BT, UK (
[email protected]) ABSTRACT
Recent developments in the remote sensing (RS) industry have opened up a huge potential for diverse applications, which has contributed to the rapidly increasing user community in developed and developing countries; and the frequency and the volume of Image Processing and Classification work has increased simultaneously. However, the number of experts in the area has not increased at the same pace. The available data, tools, and techniques are too complex for most users, unless they devote a considerable amount of time to obtaining the relevant “technical” background. Users are faced with the problems of viewing a mass of data, applying appropriate methods, evaluating the results, and handling the specific computer platform. Therefore, not only are advanced methods required for diverse and complex image processing and classification works, but an intelligent system is required, which incorporates advanced methods and reduces the dependency on experts. In this context, this paper reviews works on intelligent systems in RS image processing and classifications and identifies current requirements for the design of an intelligent RS image processing and classification system. Keywords: Intelligent System, Remote Sensing, Image processing
1. INTRODUCTION Given the scarcity of domain experts, a system is required that can provide adequate support for novices in performing the main steps of remotely sensed image processing and classification. To this end, this paper provides an overview of various peer-reviewed systems proposed in the remote sensing literature which use advanced and intelligent methodologies. It also discusses some limitations of current systems and identifies future system requirements.
1.1
Background and Motivation
A review of remote sensing (RS) technologies for earth resources mapping and monitoring reveals that digital image processing algorithms have been widely adopted and researched since images first become available from the LANDSAT mission in the early 1970s. Before that time, aerial photo interpretation techniques were the main focus of research. During the 1990s, research interest was mainly oriented towards new and diversified sources of RS data, such as high-resolution imaging, SAR imaging, and hyper-spectral satellite imagery. Consequently, considerable effort was devoted to designing advanced imaging satellites, refining digitization techniques, creating derived digital datasets (e.g. vegetation indexes), and integration with GIS. As a result, the types of imaging systems (optical, radar, lidar) and the spatial, temporal, and spectral resolutions of the images acquired by satellite systems have removed many technical barriers. These have significantly reduced the costs of images and made them more readily available to users, who may now select the type of images required for their intended applications, and acquire images relating to the dates and times that reflect their application requirements. For example, despite the difficulties of data interpretation (Woodhouse, 2000, Hu and Ban, 2008), the availability of the spaceborne Synthetic Aperture Radar (SAR) data has provided opportunities for mapping crops in all weather conditions, throughout the circadian cycle. Thus, the use of SAR system data provides some advantages over equivalent data from optical systems (Hara et al., 1994, Shao et al., 2001). It is even considered suitable for detecting changes in land use even in wet season conditions, in tropical regions such as Bangladesh, where much of the time cloud cover limits the use of optical RS (EGIS and SPARRSO, 2001). Despite these advances, a significant amount of research effort has been concentrated on finding various advanced methods of image classification for obtaining high accuracy results. A recent review by
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