BROWSE
Detail
Journal Article
Generation of Super-Resolution Benchmark Dataset for Compact Advanced Satellite 500 Imagery and Proof of Concept Results
- Authors
- Yonghyun Kim, Jisang Park, Daesub Yoon
- Issue Date
- 2023-08
- Citation
- 대한원격탐사학회지, v.39, no.4, pp.459-466
- ISSN
- 1225-6161
- Publisher
- 대한원격탐사학회
- Language
- English
- Type
- Journal Article
- Abstract
- In the last decade, artificial intelligence's dramatic advancement with the development of various deep learning techniques has significantly contributed to remote sensing fields and satellite image applications. Among many prominent areas, super-resolution research has seen substantial growth with the release of several benchmark datasets and the rise of generative adversarial network-based studies. However, most previously published remote sensing benchmark datasets represent spatial resolution within approximately 10 meters, imposing limitations when directly applying for super-resolution of small objects with cm unit spatial resolution. Furthermore, if the dataset lacks a global spatial distribution and is specialized in particular land covers, the consequent lack of feature diversity can directly impact the quantitative performance and prevent the formation of robust foundation models. To overcome these issues, this paper proposes a method to generate benchmark datasets by simulating the modulation transfer functions of the sensor. The proposed approach leverages the simulation method with a solid theoretical foundation, notably recognized in image fusion. Additionally, the generated benchmark dataset is applied to state-of-the-art super-resolution base models for quantitative and visual analysis and discusses the shortcomings of the existing datasets. Through these efforts, we anticipate that the proposed benchmark dataset will facilitate various super-resolution research shortly in Korea.
- KSP Keywords
- Benchmark datasets, Feature diversity, IMAGE FUSION, Remote sensing(RS), Simulation method, Small objects, Spatial distribution, Super resolution, Visual Analysis, artificial intelligence, deep learning(DL)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC)
(CC BY NC)
