Simple Roads Extraction Algorithm Based on Edge Detection Using Satellite Images

Darweesh, Muna; Mansoori, Saeed Al; Al-Ahmad, Hussain

doi:10.1109/icivc47709.2019.8981118

Cited by 9 publications

(1 citation statement)

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“…The development of aerospace technology and remote sensing technology has promoted the application of hyperspectral image (HSI) based on remote sensing satellite [1][2][3][4], such as land monitoring [5,6], urban planning [7], road network layout [8], agricultural yield estimation [9] and disaster prevention and control [10]. However, due to the volume limitation of the imaging system and the need for system stability and time resolution, the acquisition of a large amount of spectral band information of hyperspectral images is often at the expense of spatial resolution [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Mapping Network for Hyperspectral Image Super-Resolution

et al. 2021

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Hyperspectral Image (HSI) can continuously cover tens or even hundreds of spectral segments for each spatial pixel. Limited by the cost and commercialization requirements of remote sensing satellites, HSIs often lose a lot of information due to insufficient image spatial resolution. For the high-dimensional nature of HSIs and the correlation between the spectra, the existing Super-Resolution (SR) methods for HSIs have the problems of excessive parameter amount and insufficient information complementarity between the spectra. This paper proposes a Multi-Scale Feature Mapping Network (MSFMNet) based on the cascaded residual learning to adaptively learn the prior information of HSIs. MSFMNet simplifies each part of the network into a few simple yet effective network modules. To learn the spatial-spectral characteristics among different spectral segments, a multi-scale feature generation and fusion Multi-Scale Feature Mapping Block (MSFMB) based on wavelet transform and spatial attention mechanism is designed in MSFMNet to learn the spectral features between different spectral segments. To effectively improve the multiplexing rate of multi-level spectral features, a Multi-Level Feature Fusion Block (MLFFB) is designed to fuse the multi-level spectral features. In the image reconstruction stage, an optimized sub-pixel convolution module is used for the up-sampling of different spectral segments. Through a large number of verifications on the three general hyperspectral datasets, the superiority of this method compared with the existing hyperspectral SR methods is proved. In subjective and objective experiments, its experimental performance is better than its competitors.

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