Voting in Transfer Learning System for Ground-Based Cloud Classification
Abstract
:1. Introduction
2. Related Work
3. Materials and Methods
3.1. Image Resize
3.2. Network Design and Transfer Learning
3.3. Voting Based Learning
4. Experimental Results
4.1. Datasets
- The multimodal ground-based cloud database (MGCD) [7,25] is collected in China and consists of cloud images captured by a sky camera with a fisheye lens under a variety of conditions and multimodal cloud information. It includes a total amount of 1720 cloud data. The images are divided into seven classes: cumulus, cirrus, altocumulus, clear sky, stratus, stratocumulus, and cumulonimbus. The number of item of each class varies from 140 to 350, and the detailed numbers are listed in Table 2.
- The Singapore whole sky imaging categories database (SWIMCAT) dataset [15] is composed of 784 sky/cloud patch images with 125 × 125 pixels captured using a wide-angle high-resolution sky imaging system, a calibrated ground-based WSI designed by [26]. The dataset is split into five distinct categories: clear sky, patterned clouds, thick dark clouds, thick white clouds, and veil clouds. The details are presented in Table 3.
- The cirrus cumulus stratus nimbus (CCSN) dataset [9] contains only 2543 unique cloud images with 256 × 256 pixels in the JPEG format and contains 10 different forms in cloud observation. It is characterized by a large set of images, making it the largest of the available public cloud datasets. Details are shown in Table 4.
4.2. Results
4.3. Discussion
5. Conclusions and Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Network | Depth | Size (MB) | Parameters (Millions) | Input Size |
---|---|---|---|---|
Densenet201 | 201 | 77 | 20 | 224 × 224 |
Alexnet | 8 | 227 | 61 | 227 × 227 |
Googlenet | 8 | 27 | 7 | 224 × 224 |
Resnet18 | 18 | 44 | 11.7 | 224 × 224 |
Resnet50 | 50 | 96 | 25.6 | 224 × 224 |
Nasnetlarge | * | 332 | 88.9 | 331 × 331 |
Label | Cloud Type | Number of Samples |
---|---|---|
1 | Cumulus | 160 |
2 | Cirrus | 300 |
3 | Altocumulus | 340 |
4 | Clear sky | 350 |
5 | Stratocumulus | 250 |
6 | Stratus | 140 |
7 | Cumulonimbus | 180 |
Label | Cloud Type | Number of Samples |
---|---|---|
A | Clear Sky | 224 |
B | Patterned clouds | 89 |
C | Thick dark clouds | 251 |
D | Thick white clouds | 135 |
E | Veil clouds | 85 |
Label | Cloud Type | Number of Samples |
---|---|---|
Ci | Cirrus | 139 |
Cs | Cirrostratus | 287 |
Cc | Cirrocumulus | 268 |
Ac | Altocumulus | 221 |
As | Altostratus | 188 |
Cu | Cumulus | 182 |
Cb | Cumulonimbus | 242 |
Ns | Nimbostratus | 274 |
Sc | Stratocumulus | 340 |
St | Stratus | 202 |
Ct | Contrails | 200 |
Datasets | MGCD | SWIMCAT | CCSN | |
---|---|---|---|---|
Networks | ||||
Densenet201 | ✔ | ✔ | ✔ | |
Alexnet | ✔ | ✔ | ✔ | |
Googlenet | ✔ | ✔ | ✔ | |
Resnet18 | ✗ | ✗ | ✔ | |
Resnet50 | ✔ | ✔ | ✔ | |
Nasnetlarge | ✗ | ✗ | ✔ |
Method | Acc |
---|---|
Our | 99.98 |
MMFN [7] | 88.63 |
DCAFs + MI [7] | 82.97 |
BOVW + MI [7] | 67.20 |
PBOVW + MI [7] | 67.15 |
LPB + MI [7] | 50.53 |
CLPB + MI [7] | 69.68 |
CloudNet + MI [7] | 80.37 |
BoVW [27] | 66.15 |
PBoVW [27] | 66.13 |
LBP [28] | 55.20 |
CLBP [29] | 69.18 |
VGG-16 [30] | 77.95 |
DCAFs [8] | 82.67 |
CloudNet [9] | 79.92 |
DMF [10] | 79.05 |
DTFN [11] | 86.48 |
HMF [12] | 87.90 |
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Manzo, M.; Pellino, S. Voting in Transfer Learning System for Ground-Based Cloud Classification. Mach. Learn. Knowl. Extr. 2021, 3, 542-553. https://doi.org/10.3390/make3030028
Manzo M, Pellino S. Voting in Transfer Learning System for Ground-Based Cloud Classification. Machine Learning and Knowledge Extraction. 2021; 3(3):542-553. https://doi.org/10.3390/make3030028
Chicago/Turabian StyleManzo, Mario, and Simone Pellino. 2021. "Voting in Transfer Learning System for Ground-Based Cloud Classification" Machine Learning and Knowledge Extraction 3, no. 3: 542-553. https://doi.org/10.3390/make3030028
APA StyleManzo, M., & Pellino, S. (2021). Voting in Transfer Learning System for Ground-Based Cloud Classification. Machine Learning and Knowledge Extraction, 3(3), 542-553. https://doi.org/10.3390/make3030028