Feature Concatenation of Hyperspectral and DEM Data for Land Cover Classification

Gross, Wolfgang L.; Bulatov, Dimitri; Schreiner, Simon; Middelmann, Wolfgang

doi:10.1109/igarss39084.2020.9323131

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Research is done on material classification with other input, such as multi-spectral or hyper-spectral data, whereby the shift towards ML-based approaches using for instance spectral, texture or three-dimensional features in the recent years is noticeable. [6][7][8][9][10] However, these methods are not yet used in commercial tools.…”

Section: Introduction 11 Motivationmentioning

confidence: 99%

Synthetic thermal imagery for UAV-based reconnaissance by change detection

Strauß,

Bulatov,

Stütz

2023

Artificial Intelligence for Security and Defence Applications

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While deep learning is well-established in image analysis with regard to the visual domain, processing thermal infrared data still mainly relies on conventional methods. Regarding particularly change detection, one may even rely on statistical approaches. The reason beyond is the limited quantity of adequate thermal imagery needed to train deep learning models and the challenging characteristics of thermal imagery itself, which, unlike RGB data, is strongly dependent on the underlying materials and the temporal evolution of environmental conditions as well as scene composition. We therefore aim at generating a new dataset of synthetic thermal imagery which is specifically designed to allow the application of deep learning methods in the area of UAV-based reconnaissance by change detection. In this paper, we present our technical approach to generate this dataset and our preliminary results. We limit the simulated changes within the dataset to two objects of interest, i.e. tanks and landmines, and both object types are rather simplified for preliminary testing. We outline the state-of-the-art methodologies of generating synthetic thermal data and verify them with respect to the requirements, which we deduced from a comprehensive literature review on both deep-learning-based change detection and thermal imagery simulation. We find that the given methods do not fully comply with the requirements on thermal training data. Therefore, two datasets are generated: thermal imagery based on standard methods and thermal imagery that meets the requirements. By comparison, our preliminary findings show significant differences in image features which potentially affect the training of deep learning models.

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