Implementasi Algoritma Homogeneity Untuk Deteksi Tepi Pada Citra Pankromatik
DOI:
https://doi.org/10.64366/adajisr.v2i2.75Keywords:
Edge Detection; Panchromatic Image; HomogeneityAbstract
Remote sensing with high-resolution panchromatic imagery plays an important role in spatial analysis such as regional mapping, spatial planning, and disaster mitigation. One important stage in image processing is edge detection, which serves to clarify the boundaries of objects such as buildings, roads, and rivers. Problems that often arise in panchromatic images are noise, contrast differences, and spatial data complexity, which cause traditional edge detection methods to be less than optimal. This study proposes the use of the Homogeneity algorithm as a solution due to its simplicity in gradient calculation. This algorithm works by comparing the intensity value of the central pixel to its eight neighbours in a 3x3 kernel, then selecting the maximum difference to determine the presence of edges. The results of implementation on a 5x5 sample image show that large difference values represent edges, while small values indicate homogeneous areas. Further application on a 500x500 pixel panchromatic image produces clear and contrasting edge visualisation, allowing for better recognition of surface object boundaries. This study proves that the Homogeneity algorithm is capable of extracting image edges quickly and effectively, and can be used as an efficient alternative to complex convolution methods for real-time remote sensing applications.
Downloads
References
Y.-M. Tan, J.-Z. Huai, dan Z.-S. Tang, “An object-oriented remote sensing image segmentation approach based on edge detection,” Guang pu xue yu Guang pu fen xi= Guang pu, vol. 30, no. 6, hal. 1624–1627, 2010, [Daring]. Tersedia pada: https://europepmc.org/article/med/20707163
X. Shen, Y. Guo, dan J. Cao, “Object-based multiscale segmentation incorporating texture and edge features of high-resolution remote sensing images,” PeerJ. Computer science, vol. 9. College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China., hal. e1290, 2023. doi: https://doi.org/10.7717/peerj-cs.1290.
M. Peng dan S. Li, “Evaluation of edge detection for panchromatic remote sensing image based on ROC curve,” in MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 2007, vol. 6790, hal. 619–626. doi: https://doi.org/10.1117/12.750499.
Y. Wang, X. Li, W. Zhang, dan L. Zhang, “Building extraction of urban area from high resolution remotely sensed panchromatic data of urban area,” in Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, Nov 2008, vol. 7144, hal. 71441E. doi: 10.1117/12.812745.
L. Xia, X. Zhang, J. Zhang, H. Yang, dan T. Chen, “Building extraction from very-high-resolution remote sensing images using semi-supervised semantic edge detection,” Remote Sens., vol. 13, no. 11, hal. 2187, 2021, doi: https://doi.org/10.3390/rs13112187.
B. Guo, J. Zhang, dan X. Li, “River extraction method of remote sensing image based on edge feature fusion,” IEEE Access, vol. 11, hal. 73340–73351, 2023, doi: 10.1109/ACCESS.2023.3296641.
H. Zhang, K. Zhang, F. Wang, dan W. Qian, “Edge detection algorithm for noisy remote sensing image using directional filter,” in Journal of Physics: Conference Series, 2023, vol. 2478, no. 6, hal. 1–9. doi: 10.1088/1742-6596/2478/6/062018.
D. Prabhakar dan P. K. Garg, “Building Edge Detection from Very High-Resolution Remote Sensing Imagery Using Deep Learning,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. 48, hal. 189–196, 2023, doi: https://doi.org/10.5194/isprs-archives-XLVIII-M-3-2023-189-2023.
Y. Liao et al., “A two-stage mutual fusion network for multispectral and panchromatic image classification,” IEEE Trans. Geosci. Remote Sens., vol. 60, hal. 1–18, 2022, doi: 10.1109/TGRS.2022.3222458.
Y. Zhao, C. Xian, G. Wen, P. Huang, dan W. Ren, “Design of distributed event-triggered average tracking algorithms for homogeneous and heterogeneous multiagent systems,” IEEE Trans. Automat. Contr., vol. 67, no. 3, hal. 1269–1284, 2021, doi: 10.1109/TAC.2021.3060714.
M. I. Alhari, H. Nuraliza, dan A. A. N. Fajrillah, “Implementasi Aplikasi Smart City Pada Management Informasi Mitigasi Bencana Kekeringan,” J. Ilm. Teknol. Inf. Asia, vol. 16, no. 1, hal. 9–18, 2022, doi: https://doi.org/10.32815/jitika.v16i1.654.
R. Yan et al., “Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging,” IEEE Trans. Med. Imaging, vol. 42, no. 7, hal. 1932–1943, 2023, doi: 10.1109/TMI.2022.3233574.
R. Archana dan P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif. Intell. Rev., vol. 57, no. 1, hal. 11, 2024, doi: https://doi.org/10.1007/s10462-023-10631-z.
F. G. Cunha, T. G. Santos, dan J. Xavier, “In situ monitoring of additive manufacturing using digital image correlation: a review,” Materials (Basel)., vol. 14, no. 6, hal. 1–22, 2021, doi: https://doi.org/10.3390/ma14061511.
S. D. Rahmawati dan D. Apriyanti, “Klasifikasi Area Vegetasi dan Non Vegetasi pada Citra Sentinel-2 Menggunakan Metode EVI dengan Google Earth Engine (Studi Kasus: Kabupaten Klaten),” J. Ilm. Geomatika, vol. 3, no. 1, hal. 1–13, 2023, doi: https://doi.org/10.31315/imagi.v3i1.7484.
A. A. G. Ekayana, “Implementasi Dan Analisis Data Logger Sensor Temperature Menggunakan Web Server Berbasis Embedded System,” J. Pendidik. Teknol. dan Kejuru., vol. 17, no. 1, hal. 64–74, 2020, doi: https://doi.org/10.23887/jptk-undiksha.v17i1.22411.
H. Pangaribuan dan S. Sitohang, “Peningkatan Kualitas Deteksi Tepi dengan Metode Segmentasi Citra,” REMIK Ris. dan E-Jurnal Manaj. Inform. Komput., vol. 7, no. 1, hal. 591–601, 2023, doi: 10.33395/remik.v7i1.12050.
P. E. Debevec, C. J. Taylor, dan J. Malik, “Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach,” in Seminal Graphics Papers: Pushing the Boundaries, Volume 2, 2023, hal. 465–474. doi: https://doi.org/10.1145/3596711.3596761.
H. Rayra, Y. Yassir, dan R. Rachmawati, “Penggunaan Koreksi Gamma Dengan Metode Robert, Prewitt, Dan Sobel Untuk Penyempurnaan Gambar Pada Citra Dalam Air,” J. TEKTRO, vol. 7, no. 1, hal. 65–71, 2023, doi: http://dx.doi.org/10.30811/tektro.v7i1.3863.
M. T. Ramakrishna, V. K. Venkatesan, I. Izonin, M. Havryliuk, dan C. R. Bhat, “Homogeneous adaboost ensemble machine learning algorithms with reduced entropy on balanced data,” Entropy, vol. 25, no. 2, hal. 2–13, 2023, doi: https://doi.org/10.3390/e25020245.
R. Sianturi, “Uji homogenitas sebagai syarat pengujian analisis,” J. Pendidikan, Sains Sos. Dan Agama, vol. 8, no. 1, hal. 386–397, 2022, doi: https://doi.org/10.53565/pssa.v8i1.507.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Implementasi Algoritma Homogeneity Untuk Deteksi Tepi Pada Citra Pankromatik
ARTICLE HISTORY
How to Cite
Issue
Section
Copyright (c) 2025 Dedek Kumala Hakim

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).






