Improving Accuracy of Cloud Images using DenseNet-VGG19

Gita Fadila Fitriana, Amalia Beladinna Arifa, Agi Prasetiadi, Faisal Dharma Adhinata, Nur Ghaniaviyanto Ramadhan

Abstract


Weather classification has become a significant challenge due to the unpredictable nature of climate conditions. For farmers, predicting the start of the rainy season is very important. This is because it is related to the cost factor that must be incurred, and also, the waiting time for the harvest will have an effect if the weather is not supportive. Farmers also have to prepare seeds for the start of their farming. Therefore, farmers who start nurseries early in the rainy season will miss significant planting time. Based on these problems, this study uses a convolutional neural network (CNN) for weather classification using cloud imagery. CNN is shown to classify different spectro-temporal features of sound and is thus suitable for cloud image classification. We collect cloud image data using secondary data. Our model will use a layer based on the convolution CNN architecture with a pooling layer and a solid layer as the output layer. The cloud dataset used is 1230 data divided into five classes, namely cloudy, foggy, rain, shine, and sunrise, which we use to train our model in research for the feature extraction process using DenseNet and VGG19. We use two types of classification, namely fully connected and Global Average Pooling (GAP). Our model can achieve a classification accuracy of 90.8% DenseNet-Fully Connected from our training process. From our testing process, our model can reach 95.7% using DenseNet-Fully Connected classification accuracy. Thus, the CNN model proved very accurate in classifying cloud images.

Keywords


Cloud images; CNN; DenseNet; VGG19; weather classification

Full Text:

PDF

References


G. Chavan and B. Momin, “An Integrated approach for Weather Forecasting over Internet of Things: A Brief Review,†in 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2017, pp. 83–88, doi: 10.1109/I-SMAC.2017.8058291.

S. Rasp, P. D. Dueben, S. Scher, J. A. Weyn, S. Mouatadid, and N. Thuerey, “WeatherBench: A Benchmark Data Set for Data-Driven Weather Forecasting,†J. Adv. Model. Earth Syst., vol. 12, no. 11, pp. 1–17, 2020, doi: 10.1029/2020MS002203.

S. Kaji and S. Kida, “Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging,†Radiol. Phys. Technol., vol. 12, no. 3, pp. 235–248, 2019, doi: 10.1007/s12194-019-00520-y.

S. Scher, “Toward Data-Driven Weather and Climate Forecasting: Approximating a Simple General Circulation Model With Deep Learning,†Geophys. Res. Lett., vol. 45, no. 22, pp. 12,616-12,622, 2018, doi: 10.1029/2018GL080704.

P. Dueben and P. Bauer, “Challenges and design choices for global weather and climate models based on machine learning,†Geosci. Model Dev., vol. 11, no. 10, pp. 1–17, 2018, doi: 10.5194/gmd-2018-148.

S. H. Jeong, J. P. Yun, H. G. Yeom, H. J. Lim, J. Lee, and B. C. Kim, “Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs,†Sci. Rep., vol. 10, no. 1, pp. 1–5, 2020, doi: 10.1038/s41598-020-73287-7.

P. Hewage, M. Trovati, E. Pereira, and A. Behera, “Deep learning-based effective fine-grained weather forecasting model,†Pattern Anal. Appl., vol. 24, no. 1, pp. 343–366, 2021, doi: 10.1007/s10044-020-00898-1.

M. Elhoseiny, S. Huang, and A. Elgammal, “Weather classification with deep convolutional neural networks,†Proc. - Int. Conf. Image Process. ICIP, vol. 2015-Decem, pp. 3349–3353, 2015, doi: 10.1109/ICIP.2015.7351424.

S. ur Rehman, S. Tu, O. ur Rehman, Y. Huang, C. M. S. Magurawalage, and C. C. Chang, “Optimization of CNN through novel training strategy for visual classification problems,†Entropy, vol. 20, no. 4, pp. 1–10, 2018, doi: 10.3390/e20040290.

A. Jaiswal, N. Gianchandani, D. Singh, V. Kumar, and M. Kaur, “Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning,†J. Biomol. Struct. Dyn., vol. 39, no. 15, pp. 5682–5689, 2021, doi: 10.1080/07391102.2020.1788642.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,†Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017, doi: 10.1145/3065386.

S. ur Rehman, S. Tu, O. ur Rehman, Y. Huang, C. M. S. Magurawalage, and C. C. Chang, “Optimization of CNN through novel training strategy for visual classification problems,†Entropy, vol. 20, no. 4, pp. 1–11, 2018, doi: 10.3390/e20040290.

I.-J. Kim and X. Xie, “Handwritten Hangul recognition using deep convolutional neural networks,†Int. J. Doc. Anal. Recognit., vol. 18, no. 1, pp. 1–13, 2015, doi: 10.1007/s10032-014-0229-4.

K. Xu, W. Long, Y. Sun, and Y. Lin, “A novel image feature extraction algorithm based on the fusion AutoEncoder and CNN,†J. Phys. Conf. Ser., vol. 1646, no. 1, 2020, doi: 10.1088/1742-6596/1646/1/012039.

R. Ferdiana, W. F. Dicka, and A. Boediman, “Cat Sounds Classification with Convolutional Neural Network,†Int. J. Electr. Eng. Informatics, vol. 13, no. 3, pp. 755–765, 2021, doi: 10.15676/ijeei.2021.13.3.15.

K. W. Gunawan, A. A. Hidayat, T. W. Cenggoro, and B. Pardamean, “A Transfer Learning Strategy for Owl Sound Classification by Using Image Classification Model with Audio Spectrogram,†Int. J. Electr. Eng. Informatics, vol. 13, no. 3, pp. 546–553, 2021, doi: 10.15676/ijeei.2021.13.3.3.

V. Gupta, “Weather dataset | Kaggle,†Kaggle.com, 2020. https://www.kaggle.com/vijaygiitk/multiclass-weather-dataset (accessed Nov. 07, 2021).,†2020. .

L. W. Morton, J. M. McGuire, and A. D. Cast, “A good farmer pays attention to the weather,†Clim. Risk Manag., vol. 15, pp. 18–31, 2017, doi: 10.1016/j.crm.2016.09.002.

R. F. Rachmadi and I. K. E. Purnama, “Paralel Spatial Pyramid Convolutional Neural Network untuk Verifikasi Kekerabatan berbasis Citra Wajah,†J. Teknol. dan Sist. Komput., vol. 6, no. 4, pp. 152–157, 2018, doi: 10.14710/jtsiskom.6.4.2018.152-157.

M. Mateen, J. Wen, Nasrullah, S. Song, and Z. Huang, “Fundus image classification using VGG-19 architecture with PCA and SVD,†Symmetry (Basel)., vol. 11, no. 1, 2019, doi: 10.3390/sym11010001.

E. Alpaydin, Introduction to machine learning, vol. 1107. 2014.

W. K. Mutlag, S. K. Ali, Z. M. Aydam, and B. H. Taher, “Feature Extraction Methods: A Review,†J. Phys. Conf. Ser., vol. 1591, no. 1, 2020, doi: 10.1088/1742-6596/1591/1/012028.

M. A. Farooq, H. Javidnia, and P. Corcoran, “Performance estimation of the state-of-the-art convolution neural networks for thermal images-based gender classification system,†J. Electron. Imaging, vol. 29, no. 06, pp. 1–20, 2020, doi: 10.1117/1.jei.29.6.063004.

M. Bojarski et al., “VisualBackProp: Efficient visualization of CNNs for autonomous driving,†Proc. - IEEE Int. Conf. Robot. Autom., pp. 4701–4708, 2018, doi: 10.1109/ICRA.2018.8461053.

E. Lima, X. Sun, J. Dong, H. Wang, Y. Yang, and L. Liu, “Learning and Transferring Convolutional Neural Network Knowledge to Ocean Front Recognition,†IEEE Geosci. Remote Sens. Lett., vol. 14, no. 3, pp. 354–358, 2017, doi: 10.1109/LGRS.2016.2643000.

F. D. Adhinata, N. A. F. Tanjung, W. Widayat, G. R. Pasfica, and F. R. Satura, “Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition,†J. Ilm. Tek. Elektro Komput. dan Inform., vol. 7, no. 2, p. 230, 2021, doi: 10.26555/jiteki.v7i2.20758.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,†3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.

M. A. Farooq, H. Javidnia, and P. Corcoran, “Performance estimation of the state-of-the-art convolution neural networks for thermal images-based gender classification system,†J. Electron. Imaging, vol. 29, no. 06, pp. 1–20, 2020, doi: 10.1117/1.jei.29.6.063004.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,†Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, 2017, doi: 10.1109/CVPR.2017.243.

C. Y. Lee, S. Xie, P. W. Gallagher, Z. Zhang, and Z. Tu, “Deeply-supervised nets,†J. Mach. Learn. Res., vol. 38, pp. 562–570, 2015.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-December, pp. 2818–2826, 2016, doi: 10.1109/CVPR.2016.308.

C. Szegedy et al., “Going deeper with convolutions,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 07-12-June-2015, pp. 1–9, 2015, doi: 10.1109/CVPR.2015.7298594.

K. W. Gunawan, A. A. Hidayat, T. W. Cenggoro, and B. Pardamean, “A transfer learning strategy for owl sound classification by using image classification model with audio spectrogram,†Int. J. Electr. Eng. Informatics, vol. 13, no. 3, pp. 546–553, 2021, doi: 10.15676/IJEEI.2021.13.3.3.

S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,†Procedia Comput. Sci., vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.

T. Fawcett, “An introduction to ROC analysis,†Pattern Recognit. Lett., vol. 27, no. 8, pp. 861–874, 2006, doi: 10.1016/j.patrec.2005.10.010.

Huertas-Tato, J., Rodríguez-Benítez, F. J., Arbizu-Barrena, C., Aler-Mur, R., Galvan-Leon, I., & Pozo-Vázquez, D. (2017). Automatic cloud-type classification based on the combined use of a sky camera and a ceilometer. Journal of Geophysical Research: Atmospheres, 122, 11,045– 11,061. https://doi.org/10.1002/2017JD027131.

J. C. Villarreal Guerra, Z. Khanam, S. Ehsan, R. Stolkin and K. McDonald-Maier, "Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks," 2018 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2018, pp. 305-310, doi: 10.1109/AHS.2018.8541482.

Afzali Gorooh, Vesta, Subodh Kalia, Phu Nguyen, Kuo-lin Hsu, Soroosh Sorooshian, Sangram Ganguly, and Ramakrishna R. Nemani. 2020. "Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS" Remote Sensing 12, no. 2: 316. https://doi.org/10.3390/rs12020316.




DOI: http://dx.doi.org/10.18517/ijaseit.13.2.18293

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development