Remote Sensing Classification
Abstract
Remote-sensing classification is a complex process and requires consideration of many factors. This paper investigates the process of remote sensing classification. Importance is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. This paper suggests that designing a suitable image processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. The major steps of image classification may include determination of a suitable classification system, selection of training samples, image pre-processing, feature extraction, selection of suitable classification approaches, post-classification processing, and
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Scientists and practitioners have made great efforts in developing advanced classification approaches and techniques for improving classification accuracy However, classifying remotely sensed data into a thematic map remains a challenge because many factors, such as the complexity of the landscape in a study area, selected remotely sensed data, and image-processing and classification approaches, may affect the success of a classification. Although much previous research and some books are specifically concerned with image classification, a comprehensive up-to-date review of classification approaches and techniques is not available. Continuous emergence of new classification algorithms and techniques in recent years necessitates such a review, which will be highly valuable for guiding or selecting a suitable classification procedure for a specific study.
2. Classification process of remote sensing
Remote-sensing classification is a difficult process and requires consideration of many factors. The major steps of image classification may include determination of a suitable classification system, selection of training samples, image pre-processing,
Feature extraction, selection of suitable classification approaches, post-classification
Processing and accuracy assessment.
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The selection of suitable sensor data is the first important step for a successful classification for a specific purpose. It requires considering such factors as user’s need, the scale and characteristics of a study area, the availability of various image data and their characteristics, cost and time constraints, and the analyst’s experience in using the selected image. Scale, image resolution, and the user’s need are the most important factors affecting the selection of remotely sensed data. The user’s need determines the nature of classification and the scale of the study area, thus affecting the selection of suitable spatial resolution of remotely sensed data. Previous research has explored the impacts of scale and resolution on remote-sensing image
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