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In recent years, high-resolution satellite (HRS) images have become an important source of data for extracting geo-spatial information. A deep understanding of human cognitive capabilities is required in order to automate the method of information retrieval from HRS images. The aim of this study is to emulate human cognitive processes by integrating cognitive task analysis for information extraction from HRS images. First, the preliminary knowledge about the cognitive processes which human beings acquire during the interpretation of satellite images is collected. Then, knowledge is represented in the form of rules which are based on the visual interpretation of the images by the human beings. During knowledge elicitation these rules are used to extract buildings from HRS images utilizing the mixture tuned matched filtering algorithm. Later, the method is tested using 14 HRS images of an urban area. The average of precision, recall and F-score is computed as 79.45%, 64.34% and 70.28% respectively.

Keywords

Building Detection, Cognitive Processes, High-Resolution Satellite Images, Urban Areas.

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Building detection methods from remotely sensed images

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Authors

Naveen Chandra ¹, Himadri Vaidya ²

Affiliations
1 Wadia Institute of Himalayan Geology, 33-GMS Road, Dehradun 248 001, IN
2 Formerly Uttarakhand Technical University, Dehradun 248 007, IN

Source

Current Science, Vol 122, No 11 (2022), Pagination: 1252-1267

Abstract

With the availability of high-resolution satellite imagery, new applications have been developed for solving geospatial issues in urban regions. Building detection from remote sensing images has been an active area of research due to its broad range of applications, including city modelling, map updating and urban monitoring. The manual processing of an image is a time-consuming and laborious task. Therefore, researchers have developed methods that involve less or no human effort. At present, building detection has improved through various automated and semi-automated methods/algorithms/ techniques suggested in various studies. The objective of the present study is to review the efforts of such studies. Here, the building detection methods are categorized into six groups: (i) low-level feature-based methods, (ii) snake models, (iii) graph-based methods, (iv) shadow detection-based methods, (v) cognition-based methods and (vi) deep learning models. We hope that this study will aid the researchers working in this domain.

Keywords

Building detection, classification, geospatial issues, remote sensing images, urban areas.

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