Patcharin Kamsing
Biographic Data
| ID | 9916620 |
|---|---|
| NAME | Patcharin Kamsing |
| GIVEN NAMES | Patcharin |
| FAMILY NAME | Kamsing |
| SIGNATURE | KAMSING P |
| AFFILIATIONS | King Mongkut's Institute of Technology Ladkrabang |
| ORCID | 0000-0001-6656-8406 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet
All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for r…
Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use datase…
No prominent works on this page.
Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery
Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use datase…
Day–Night All-Sky Scene Classification with an Attention-Enhanced EfficientNet
All-sky cameras provide continuous hemispherical observations essential for atmospheric monitoring and observatory operations; however, automated classification of sky conditions in tropical environments remains challenging due to strong illumination variability, atmospheric scattering, and overlapping thin-cloud structures. This study proposes EfficientNet-Attention-SPP Multi-scale Network (EASMNet), a physics-aware deep learning framework for r…
Remote Sensing in Agriculture (2 works) · Artificial Intelligence (1 works) · Atmospheric correction (1 works) · Computer Science (1 works) · Convolutional neural network (1 works) · Deep learning (1 works) · Geography (1 works) · Impact of Light on Environment and Health (1 works) · Land cover (1 works) · Land use (1 works)