Mangrove Forest Classification in Drone Images Using HSV Color Moment and Haralick Features Extraction with K-Nearest Neighbor

Agus Wahyu Widodo, Deo Hernando, Wayan Firdaus Mahmudy

Abstract


Due to the problems with uncontrolled changes in mangrove forests, a forest function management and supervision is required. The form of mangrove forest management carried out in this study is to measure the area of mangrove forests by observing the forests using drones or crewless aircraft. Drones are used to take photos because they can capture vast mangrove forests with high resolution. The drone was flown over above the mangrove forest and took several photos. The method used in this study is extracting color features using mean values, standard deviations, and skewness in the HSV color space and texture feature extraction with Haralick features. The classification method used is the k-nearest neighbor method. This study conducted three tests, namely testing the accuracy of the system, testing the distance method used in the k-nearest neighbor classification method, and testing the k value. Based on the results of the three tests above, three conclusions obtained. The first conclusion is that the classification system produces an accuracy of 84%. The second conclusion is that the distance method used in the k-nearest neighbor classification method influences the accuracy of the system. The distance method that produces the highest accuracy is the Euclidean distance method with an accuracy of 84%. The third conclusion is that the k value used in the k-nearest neighbor classification method influences the accuracy of the system. The k-value that produces the highest accuracy is k = 3, with an accuracy of 84%.

Keywords


Mangrove Forest Classification Haralick Features HSV Color Moment; K-Nearest Neighbor; Drone Image

Full Text:

PDF

References


Bayyan, M. M. (2019). Penggunaan Unmanned Aerial Vehicle (UAV) untuk Pemetaan Mangrove di Kawasan Mangrove Bagek Kembar, Sekotong, Lombok, Nusa Tenggara Barat.

Faramondi, L., Oliva, G., Ardito, L., Crescenzi, A., Caricato, M., Tesei, M., Setola, R. (2019). Use of Drone to Improve Healthcare Efficiency and Sustainability. Opatjia: Research Gate. Retrieved November 20, 2019, from https://www.researchgate.net/publication/334521353

Pratiwi, D., Lussiana, E. T., & Madenda, S. (2013). Image Color Extraction of Forest. International Journal of Computer Applications. 64, pp. 13-16. Research Gate. doi:10.5120/10661-5430

Bekkari, A., Mammas, D., Idbraim, S., & Elhassouny, A. (2012). SVM and Haralick Features for Classification of High Resolution Satellite Images from Urban Areas. Proceedings of the 5th international conference on Image and Signal Processing (pp. 17-26). Research Gate. doi:10.1007/978-3-642-31254-0_3

Bhatia, N., & Vandana. (2010). Survey of Nearest Neighbor Techniques. International Journal of Computer Science and Information Security (IJCSIS) 2010, 8, 302-305.

Novyanti, O. E. (2019, May). Pengenalan Citra Jenis Makanan Menggunakan Ekstraksi Fitur Color Channel dan Gray Level Co-Occurence Matrix. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 3, 4234-4241.

Mosbah, M., & Boucheham, B. (2014, March). The Influence of The Color Model on The Performance of a CBIR System Based on Color Moments. Journal of Communication and Computer, 11, 266-273.

Simonthomas, S., Asharaf, P., & Thulasi, N. (2014). Automated Diagnosis of Glaucoma using Haralick Texture Features. International Conference on Information Communication and Embedded System (ICICES2014) (pp. 12-17). Chennai, India: IEEE. doi:10.1109/ICICES.2014.7033743

Loesdau, M., Chabrier, S., & Gabillon, A. (2014). Hue and Saturation in the RGB Color Space. ICISP 2014. 8509, pp. 203-212. Cherbourg, France: Research Gate. doi:10.1007/978-3-319-07998-1_23

Kunaver, M., & Tasic, J. F. (2005). Image Feature Extraction - an overview. EUROCON 2005 (pp. 183-186). Belgrade, Serbia: IEEE. doi:10.1109/EURCON.2005.1629889

Mazumder, J., Nahar, L. N., & Atique, M. U. (2018, August 8). Finger Gesture Detection and Application Using Hue Saturation Value. International Journal of Image, Graphics and Signal Processing, 31-38. doi:10.51815/ijigsp.2018.08.04

Halim, A., Hardy, DewI, C., & Angkasa, S. (2013, October). Aplikasi Image Retrieval Menggunakan Kombinasi Metode Color Moment dan Bagor Texture. Mikroskil, 14, 109-117. Retrieved November 20, 2019, from https://www.mikroskil.ac.id/ejurnal/index.php/jsm/article/view/124

Valentin, M. B., Bom, C. R., Albuquerque, M. P., Faria, E. L., & Correia, M. D. (2016, April 1). Texture Classification Based on Spectral Analysis and Haralick Features. CBPF, 6, 28-61. doi:10.7437/NT2236-7640/2016.01.004

Subban, R., Susitha, N., & Mankame, D. P. (2018, March). Efficient Iris Recognition using Haralick Features Based Extraction and Fuzzy Particle Swarm Optimization. Cluster Computing, 21(1), 79-90. doi:10.1007/s10586-017-0934-0

Rahmat, R. F., Aruan, T., Purnawati, S., Faza, S., Lini, T. Z., & Onrizal. (2018). Fungus Image Identification using K-Nearest Neighbor. IOP Conference Series: Materials Science and Engineering (pp. 1-7). Medan, Indonesia: IOP Publishing. doi:10.1088/1757-899X/420/1/012097




DOI: https://doi.org/10.31763/simple.v1i3.6

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Agus Wahyu Widodo, Deo Hernando, Wayan Firdaus Mahmudy

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


SIGNAL AND IMAGE PROCESSING LETTERS (SIMPLE)

ISSN Online: 2714-6677 | Print: 2714-6669
Published by Association for Scientific Computing Electrical and Engineering (ASCEE)
Website : https://simple.ascee.org/index.php/simple/
Email 1 : simple@ascee.org
Email 2 : azhari@ascee.org


 

View My Stats