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IRS-1B based FCC image interpretation for land use and land cover mapping: An expert system approach


Rajkishore Prasad
Lecturer, University Deptt. of Electronics, B. R. A.
Bihar University, Muzzafarpur
rkishore2k@hotmail.com


A. K. Sinha
Professor Department of Electrical Engineering,
MIT, Muzzafarpur
aksinha_1@yahoo.com

Introduction
The science and art of remote sensing has emerged as one of the most important application of space technology in obtaining environmental and natural resources data related to earth using various aerial platforms. These data are being used in large number of areas e.g. agriculture, forestry, archeology, geography, geology, oceanography, ecology etc to obtain useful information for the future planning, management and sustainable development of the natural resources. Such data are collected using different aerial platforms. However, the satellite based remote sensing has become very popular and different countries have launched remote-sensing satellites, for this purpose. The collected data are processed and interpreted in different forms and formats using digital techniques or optical techniques to extract useful information. One of the most widely used data format for information extraction is the infrared False Color Composite (FCC) image. The extraction of information from such images about ground reality is done by image interpretation for which generally three methods namely photo interpretation, spectral analysis and data integration are used. However, for the public, satellite images and the information they bear seem vague and far beyond the understanding of a layman. In the coming decades remote sensing will be one of the key tool for making critical decisions affecting the earth, its environment and resources. So it is important that the informed citizens should have a basic understanding of these technologies and how they are used. Such awareness in mass will ensure the credibility and faith in the accuracy of information derived from the remote sensor data.

In this paper our main concern is with photo interpretation method of IRS-1B standard FCC and to describe the development of a rule based expert system, using expert system shell ESTA (Expert System shell for Text Animation),(Fig.1) for the beginners to learn the visual interpretation skill for the purpose of land use & land cover mapping.


Fig. 1: Expert system shell to expert system

Expert system and FCC image interpretation
Photo interpretation is the visual interpretation of images based on features tone, pattern, shape, size, shadow, texture and association. Most of the conventional digital image processing techniques is based on color or size or texture or tonal variation of each pixel in the image. In contrast to digital analysis of the images, a human interpreter does not interpret the image pixel by pixel. Instead he or she exploits the aggregate information related to various basic image-features of unknown objects along with his scientific knowledge, general knowledge of the phenomena as well as experience to do classification [1,2]. As a consequence, the interpretation result for land use and land cover produced by a well-trained human interpreter is often less crude than the same obtained using digital techniques [3]. For human interpreter it is easy to interpret natural color image but the interpretation of FCC image becomes difficult and requires adequate training and experience [4]. Also different band combinations of satellite data for three primary colors result in different FCC images, which are suitable for different application. Every application of remote sensing deals with a specific subject or integrated process of different subjects. Thus the process of visual interpretation of wide variety of remotely sensed data is a complex intuitive process of combining evidential information from different sources and subjecting such information to an expert’s knowledge, experience and heuristics at each levels namely detection, identification, analysis, recognition and classification of the process. It calls for analysis of a number of related information by a domain expert. So even with on going advances in digital image processing technique the importance and role of human photo interpreter can’t be denied and it is required to train human resources in this skill. Unfortunately, this art has not been taught systematically in the related educational institutions from the last 20 years. As a result this art is being lost [5]. However, in future the scope of on-screen interpretation of high resolution remote sensing data will increase and image identification system providing way to combine together human interpreter and machine interpretation will be required as the maximum interpretation accuracy achieved using digital image processing technique has been reported up to 70 to 80% [6]. The associated information and the logical reasoning that are used by a well-trained human interpreter can be encoded in the from of rules and facts to create knowledge based systems. The need of expert level performance in the remote sensing image interpretation has brought in a shift from domain independent statistical methods to domain specific knowledge based techniques [7]. Thus the activity of image interpretation has similarity with the nature of explorative and qualitative reasoning in the line of artificial intelligence and expert system. Since the interpretation of each FCC images requires different skill and experience so for a human interpreter it becomes difficult to manage properly application of huge knowledge while making decision in photo interpretation. Also the human interpreter may be absent or not easily available. The interpretation by a human is also effected by his mental status like biases, anxieties, fatigue, repeatability of task etc. So the need of computer based support is obvious. The knowledge used in photo interpretation can be represented in logical paradigm that makes prolog [8] based expert system shell ESTA suitable for expert system development. Lot of image interpretation system has been developed in the past. These system are different in number of ways like the knowledge representation scheme, control strategy, application area etc. Most of them are based on spectral features of the image. Very few systems have been developed for developing visual interpretation skill. In the field of land use /land categorization using satellite data a rule based expert system [9] was developed using visual interpretation keys developed by NRSA (National Remote Sensing Agency), India. In other development an expert system for “Land Form Study” has been created, in the Prolog language, for landform type identification and classification in automated and user interactive way [10]. Majority of systems have been developed for automatic image interpretation e.g. the system SPAM [11] for aerial image interpretation is based on production rules. The system MESSIE [12] has used production rules as well as semantic nets. In AIDA system [13] semantic nets and rules has been used to represent knowledge base while ERNEST [14] uses semantic net only. SIGMA [15] contains three expert modules based on frame and rule representation. Recently Jenson et al (16) in his neural network based photo interpretation approach have emphasized use of hybrid approach i.e. visual interpretation and digital interpretation. However, in the neural network based system the classification rules are hidden in its weight and is not useful for a user for learning point of view.

This paper is organized as follows. In the following section and its subjection the proposed expert system development process using ESTA has been described. In the section 4, consultation with the system and result has been presented using standard FCC images produced by IRS-1B & 1D satellites data. The last section contains conclusion and future work.


Fig. 2: Section tree of the system

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