Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

  • Nailus Sa'ada Politeknik Elektronika Negeri Surabaya, Indonesia
  • Tri Harsono Politeknik Elektronika Negeri Surabaya, Indonesia
  • Ahmad Basuki Politeknik Elektronika Negeri Surabaya, Indonesia
Keywords: Satellite Image, Segmentation Performance, Feature extraction, Wirlwind, DBSCAN Clustering


Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.


Download data is not yet available.

Author Biographies

Nailus Sa'ada, Politeknik Elektronika Negeri Surabaya, Indonesia

Master Course of Information Technology Department

Tri Harsono, Politeknik Elektronika Negeri Surabaya, Indonesia

Information Technology Department

Ahmad Basuki, Politeknik Elektronika Negeri Surabaya, Indonesia

Information Technology Department


Shalini Bhatia, Kumkum Saxena, Satellite Image Segmentation using Watershed based Algorithms, International Conference on Soft computing and Intelligent Systems, Bali, 2007.

Song Yuheng, Yan Hao, Image Segmentation Algorithms Overview, arXiv preprint arXiv: 1707.02051, 2017.

Dilpreet Kaur, Yadwinder Kaur, Various Image Segmentation

Techniques: A Review, International Journal of Computer Science and Mobile Computing, Vol. 3, No. 5 , pp.809 – 814, 2014.

Chris A Glasbey, Graham W Horgan, Image analysis for the biological sciences, John Wiley & Sons, Inc (New York), pp. 93-84, 1995.

Kaliyamurthie K.P, Parameswari D, Remote Sensing Imaging for Satellite Image, Indian Journal of Science and Technology, Vol. 8, 2015.

Peak, James E., and Paul M. Tag, Segmentation of Satellite Imagery Using Hierarchical Thresholding and Neural Networks, Journal of Applied Meteorology, Vol. 33, No. 5, pp. 605-616, 1994.

Nailussa’ada, Tri Harson, Achmad Basuki, Cloud Satellite Image Segmentation using Meng Hee Heng K-Means and DBSCAN Clustering, 2018 International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC), Bali, pp. 367-371, 2018.

Biplab Banerjee, Surender Varma G, Krishna Mohan Buddhiraju, Satellite Image Segmentation: A Novel Adaptive Mean-Shift Clustering Based Approach, 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, pp. 4319 – 4322, 2012.

Packyanathan Ganesan, Rajini V, Assessment of satellite image segmentation in RGB and HSV color space using image quality measures, 2014 International Conference on Advances in Electrical Engineering (ICAEE), Tamilnadu, pp. 1-5, 2014.

Maria Vakalopoulou, Konstantinos Karantzalos, Nikos Komodakis, Nikos Paragios, Building detection in very high resolution multispectral data with deep learning features, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, pp. 1873-1876, 2015

Packyanathan Ganesan, V Kalist, B. S. Sathish, Histogram based hill climbing optimization for the segmentation of region of interest in satellite images, 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, pp. 1-5, 2016.

Rhoma Cahyanti, Rendra Budi Hutama, Rafi Haidar Ramdlon, Windasari Dwiastuti, Fadilah Fahrul Hardiansyah, Achmad Basuki, Whirlwind Prediction using Cloud Movement Patterns on Satellite Image, 2017 International Electronics Symposium on Knowledge Creation and Intelligent Computing, Surabaya, pp. 252-257, 2017.

Robert M. Haralick, Linda G. Shapiro, Image Segmentation Techniques,Computer Vision Graphics and Image Processing, pp. 100-132. 1985.

Rafael C. Gonzales, Paul Wintz, Digital Image Processing, Addison-Wesley (United States of America), Ed. 2, 1987.

Deepak Jain, Manoj Singh, Dr. Arvind K. Sharma, Performance Enhancement of DBSCAN Density based Clustering Algorithm in Data Mining, International Conference on Energy, Communication, Data Analytics and Soft Computing, 2017.

Huan Yu, Wenhui Zhang, DBSCAN Data Clustering Algorithm for Video Stabilizing System, 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), Shenyang, 2013.

Dayang Sun, Binbin Li, Zhihong Qian, Research of Vehicle Counting Based on DBSCAN in Video Analysis, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, pp. 1523-1527.

How to Cite
Sa’ada, N., Harsono, T., & Basuki, A. (2019). Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm. EMITTER International Journal of Engineering Technology, 7(1), 301-325.