Reconstructing historical urban landscapes from KH-9 HEXAGON mapping camera system imagery: an example of Hangzhou City

Shahtahmassebi, A.R., Huang, D., Lu, J., Li, E., Huang, X., Jiang, L., Shahtahmassebi, G. ORCID: 0000-0002-0630-2750, Moore, N. and Atkinson, P.M., 2024. Reconstructing historical urban landscapes from KH-9 HEXAGON mapping camera system imagery: an example of Hangzhou City. Remote Sensing Letters, 15 (3), pp. 280-290. ISSN 2150-704X

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Abstract

Declassified images from the Keyhole (KH)-9 HEXAGON mapping camera system (MCS) offer fine-scale details of urban regions. However, these images have seldom been utilized in urban research due to challenges in labelling (collecting training samples), having only a single panchromatic band and classification. To tackle these limitations, this paper focuses on developing a multi-stage reconstructed historical fine-scale urban landscape (RHFUL) pipeline for KH-9 HEXAGON MCS. The proposed pipeline first integrates internalized parameters, hierarchical object-based image analysis properties and class variability to synthesize new features, abbreviated to IHC. Second, the pipeline uses a weak semi-automated supervised labelling (WSSL) approach to acquire training samples. Finally, the training samples and generated features are subjected to the SegNet deep learning architecture. The performance of each step was assessed against corresponding state-of-the-art benchmark approaches for each of synthesizing features, labelling and classification. In the proposed RHFUL pipeline, the proposed IHC provided the most salient information for urban classification, WSSL labelled urban features more accurately, and the SegNet architecture classified more accurately the urban features relative to the benchmarks. Considering the potential advantages, but also limitations of KH-9 HEXAGON MCS images, further research should be undertaken, particularly drawing on the current advances in pattern recognition techniques for contemporary digital satellite sensors.

Item Type:	Journal article
Publication Title:	Remote Sensing Letters
Creators:	Shahtahmassebi, A.R., Huang, D., Lu, J., Li, E., Huang, X., Jiang, L., Shahtahmassebi, G., Moore, N. and Atkinson, P.M.
Publisher:	Taylor & Francis
Date:	2024
Volume:	15
Number:	3
ISSN:	2150-704X
Identifiers:	Number Type 10.1080/2150704x.2024.2320176 DOI 1870378 Other
Rights:	This is an Accepted Manuscript of an article published by Taylor & Francis in Remote Sensing Letters on 26 February 2024, available online: https://doi.org/10.1080/2150704X.2024.2320176
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	12 Mar 2024 10:57
Last Modified:	12 Mar 2024 10:57
URI:	https://irep.ntu.ac.uk/id/eprint/51053

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