KaydedilenlerDerginizi ekleyin

Dergiler / International Journal of Engineering and Geosciences / 2026 / Volume: 10 Issue: 2

Prediction of Land Image Classification using Squeeze Synchronization layer and Convolve Craft Focus Module in ResNet 101 Model

Fatih Celik(YILDIZ TECHNICAL UNIVERSITY)0000-0001-5763-0562
Kemal Çelik(GUMUSHANE UNIVERSITY, GUMUSHANE FACULTY OF ENGINEERING)0000-0003-0662-5901
Sayfa
—
Yayın Tarihi
2025-07-15
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The accuracy value of the proposed system is 96% of accuracy, 100% of precision, 100% of recall, and 100% of F1-score, signifies the superior efficiency of the proposed model

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The classification of image is essential to make LULC (Land Use Land Cover) maps.
📊
Temel Bulgular & Veri:Recently, deep learning (DL) techniques have achieved outstanding performance in the classification of high-resolution images.
💡
Sonuç & Katkı:The accuracy value of the proposed system is 96% of accuracy, 100% of precision, 100% of recall, and 100% of F1-score, signifies the superior efficiency of the proposed model
🔬Tür:Laboratuvar & Saha Deneyi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:remote sensing · land use · land cover

Abstract

The classification of image is essential to make LULC (Land Use Land Cover) maps. However, the classification of land cover plays a vital role for studying and modernizing the land areas. Recently, deep learning (DL) techniques have achieved outstanding performance in the classification of high-resolution images. Different techniques have been employed in traditional methods to identify LULC due to its complex and ever-changing nature. However, these studies have shown improved outcomes despite some restrictions such as inaccuracies and reduced performance. To address these problems, the proposed study introduces a Squeeze Synchronization Layer (SSL) and a Convolve Craft Focus Module (CCFM) where, SSL reduces input data complexity by removing noise and irrelevant information from images using pooling and convolutional operations also, CCFM enhances feature extraction to improve land classification accuracy. The EUROSAT land image dataset is utilized for the evaluation of the introduced model. Whereas, the dataset comprises of 64x64 images, which are captured by satellite Sentinel-2A in ResNet 101 input layer. Although, a SSL is suggested, and a CCFM is implemented in the convolutional layer for classifying land images. However, the efficiency of the system is evaluated by measuring performance metrics such as recall, F1-score, precision, and accuracy values of the proposed system. The accuracy value of the proposed system is 96% of accuracy, 100% of precision, 100% of recall, and 100% of F1-score, signifies the superior efficiency of the proposed model

🔖

Bu Makaleye Atıf Yap

Celik, F., Çelik, K. (2025). Prediction of Land Image Classification using Squeeze Synchronization layer and Convolve Craft Focus Module in ResNet 101 Model. International Journal of Engineering and Geosciences, Volume: 10 Issue: 2. https://doi.org/10.26833/ijeg.1538708

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (43)

  1. Ayalke, Z., & Şişman, A. (2024). Google Earth Engine kullanılarak makine öğrenmesi tabanlı iyileştirilmiş arazi örtüsü sınıflandırması: Atakum, Samsun örneği. Geomatik. https://doi.org/10.29128/geomatik.1472160
  2. Esen, Ö., Çay, T., & Toklu, N. (2017). Evaluation Of Land Reform Policies In Turkey. International Journal Of Engineering And Geosciences, 2(2), 61–67. https://doi.org/10.26833/ijeg.297223
  3. Noi Phan, T., Kuch, V., & Lehnert, L. W. (2020). Land cover classification using google earth engine and random forest classifier-the role of image composition. Remote Sensing, 12(15). https://doi.org/10.3390/RS12152411
  4. Serwa, A. (2020). Studying the Potentiality of Using Digital Gaussian Pyramids in Multi-spectral Satellites Images Classification. Journal of the Indian Society of Remote Sensing, 48(12), 1651–1660. https://doi.org/10.1007/s12524-020-01173-w
  5. Liu, C., Zeng, D., Wu, H., Wang, Y., Jia, S., & Xin, L. (2020). Urban land cover classification of high-resolution aerial imagery using a relation-enhanced multiscale convolutional network. Remote Sensing, 12(2). https://doi.org/10.3390/rs12020311
  6. Isazade, V., Isazade, E., Qasimi, A. B., & Serwa, A. (2023). Integrating Passive and Active Remote Sensing Data with Spatial Filters for Urban Growth Analysis in Urmia, Iran. Russian Journal of Earth Sciences, 23(5). https://doi.org/10.2205/2023ES000861
  7. Zhao, S., Tu, K., Ye, S., Tang, H., Hu, Y., & Xie, C. (2023, November 3). Land Use and Land Cover Classification Meets Deep Learning: A Review. Sensors (Basel, Switzerland). https://doi.org/10.3390/s23218966
  8. Paul, S. (2022). Change detection and future change prediction in Habra I and II block using remote sensing and GIS – A case study. International Journal of Engineering and Geosciences, 7(2), 191–207. https://doi.org/10.26833/ijeg.975222
  9. Unel, F. B., Kusak, L., & Yakar, M. (2023). GeoValueIndex map of public property assets generating via Analytic Hierarchy Process and Geographic Information System for Mass Appraisal: GeoValueIndex. Aestimum, 82, 51-69
  10. Çay, T., & Satılmış, R. Y. (2024). Economic Analysis of Land Consolidation Project: Kızılcabölük Neighborhood, Tavas- Denizli- Turkey Province. International Journal of Engineering and Geosciences. https://doi.org/10.26833/ijeg.1429522
  11. Babu, R. G., Maheswari, K. U., Zarro, C., Parameshachari, B. D., & Ullo, S. L. (2020). Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images. Remote Sensing, 12(24), 1–28. https://doi.org/10.3390/rs12244135
  12. Hazer, A., Bozdağ, A., & Atasever, Ü. H. (2024). Hiper-Optimize Edilmiş Makine Öğrenim Teknikleri ile Taşınmaz Değerlemesi, Yozgat Kenti Örneği. Geomatik. https://doi.org/10.29128/geomatik.1454915
  13. Eyi, G., & Buğdaycı, İ. (2024). Uzaktan Algılama Yöntemleri ile Yangın Şiddetinin Tespiti: Yunanistan Rodos Adası Orman Yangını Örneği. Geomatik. https://doi.org/10.29128/geomatik.1481708
  14. Güngör, R., & İnam, Ş. (2019). İmar Uygulamalarında Farklı Dağıtım Metotlarının Karşılaştırılması. Geomatik, 4(3), 254–263. https://doi.org/10.29128/geomatik.548592
  15. Yu, J., Zeng, P., Yu, Y., Yu, H., Huang, L., & Zhou, D. (2022). A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data. Remote Sensing, 14(5). https://doi.org/10.3390/rs14051128
  16. Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11). https://doi.org/10.3390/rs14112654
  17. Savanović, R., & Savanović, M. (2024). The need for renewal of the real estate cadastre on the territory of Vojvodina, Republic of Serbia. International Journal of Engineering and Geosciences. https://doi.org/10.26833/ijeg.1422964
  18. Abdi, A. M. (2020). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIScience and Remote Sensing, 57(1), 1–20. https://doi.org/10.1080/15481603.2019.1650447
  19. Şenol, H. İ., Kaya, Y., Yiğit, A. Y., & Yakar, M. (2024). Extraction and geospatial analysis of the Hersek Lagoon shoreline with Sentinel-2 satellite data. Survey Review, 56(397), 367-382.
  20. Neupane, B., Horanont, T., & Aryal, J. (2021, February 2). Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sensing. MDPI AG. https://doi.org/10.3390/rs13040808
  21. Boonpook, W., Tan, Y., Nardkulpat, A., Torsri, K., Torteeka, P., Kamsing, P., … Jainaen, M. (2023). Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery. ISPRS International Journal of Geo-Information, 12(1). https://doi.org/10.3390/ijgi12010014
  22. Rajmohan, G., Chinnappan, C. V., John William, A. D., Chandrakrishan Balakrishnan, S., Anand Muthu, B., & Manogaran, G. (2021). Revamping land coverage analysis using aerial satellite image mapping. Transactions on Emerging Telecommunications Technologies, 32(7). https://doi.org/10.1002/ett.3927
  23. Faisal, A. Al, Kafy, A. A., Al Rakib, A., Akter, K. S., Jahir, D. M. A., Sikdar, M. S., … Rahman, M. M. (2021). Assessing and predicting land use/land cover, land surface temperature and urban thermal field variance index using Landsat imagery for Dhaka Metropolitan area. Environmental Challenges, 4. https://doi.org/10.1016/j.envc.2021.100192
  24. Tariq, A., & Shu, H. (2020). CA-Markov chain analysis of seasonal land surface temperature and land use landcover change using optical multi-temporal satellite data of Faisalabad, Pakistan. Remote Sensing, 12(20), 1–23. https://doi.org/10.3390/rs12203402
  25. Lv, Z., Huang, H., Sun, W., Jia, M., Benediktsson, J. A., & Chen, F. (2023). Iterative Training Sample Augmentation for Enhancing Land Cover Change Detection Performance With Deep Learning Neural Network. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2023.3282935
  26. Dinda, S., Das Chatterjee, N., & Ghosh, S. (2021). An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: A GIS-based analysis. Ecological Indicators, 121. https://doi.org/10.1016/j.ecolind.2020.107178
  27. Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based Markov Chain model. Sustainability (Switzerland), 13(2), 1–22. https://doi.org/10.3390/su13020471
  28. Kadı, F., & Yılmaz, O. S. (2024). Determination of alternative forest road routes using produced landslide susceptibility maps: A case study of Tonya (Trabzon), Türkiye. International Journal of Engineering and Geosciences, 9(2), 147–164. https://doi.org/10.26833/ijeg.1355615
  29. Yilmaz, O. S., Gülgen, F., Güngör, R., & Kadi, F. (2018). Coğrafi Bilgi Sistemleri ve Uzaktan Algılama Teknikleri İle Arazi Kullanım Değişiminin İncelenmesi, Köprübaşı İlçesi Örneği. Geomatik, 3(3), 233–241. https://doi.org/10.29128/geomatik.410987
  30. Law, S., Seresinhe, C. I., Shen, Y., & Gutierrez-Roig, M. (2020). Street-Frontage-Net: urban image classification using deep convolutional neural networks. International Journal of Geographical Information Science, 34(4), 681–707. https://doi.org/10.1080/13658816.2018.1555832
  31. Dastour, H., & Hassan, Q. K. (2023). A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification. Sustainability (Switzerland), 15(10). https://doi.org/10.3390/su15107854
  32. Abijith, D., & Saravanan, S. (2022). Assessment of land use and land cover change detection and prediction using remote sensing and CA Markov in the northern coastal districts of Tamil Nadu, India. Environmental Science and Pollution Research, 29(57), 86055–86067. https://doi.org/10.1007/s11356-021-15782-6
  33. Uddin, M. S., Mahalder, B., & Mahalder, D. (2023). Assessment of Land Use Land Cover Changes and Future Predictions Using CA-ANN Simulation for Gazipur City Corporation, Bangladesh. Sustainability (Switzerland), 15(16). https://doi.org/10.3390/su151612329
  34. Martins, V. S., Kaleita, A. L., Gelder, B. K., da Silveira, H. L. F., & Abe, C. A. (2020). Exploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 56–73. https://doi.org/10.1016/j.isprsjprs.2020.08.004
  35. Dhanaraj, K., & Angadi, D. P. (2022). Land use land cover mapping and monitoring urban growth using remote sensing and GIS techniques in Mangaluru, India. GeoJournal, 87(2), 1133–1159. https://doi.org/10.1007/s10708-020-10302-4
  36. Tariq, A., Yan, J., & Mumtaz, F. (2022). Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan. Physics and Chemistry of the Earth, 128. https://doi.org/10.1016/j.pce.2022.103286
  37. Xu, L., Liu, X., Tong, D., Liu, Z., Yin, L., & Zheng, W. (2022). Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model. Land, 11(5). https://doi.org/10.3390/land11050652
  38. Rahnama, M. R. (2021, January 1). Forecasting land-use changes in Mashhad Metropolitan area using Cellular Automata and Markov chain model for 2016-2030. Sustainable Cities and Society. Elsevier Ltd. https://doi.org/10.1016/j.scs.2020.102548
  39. Okwuashi, O., & Ndehedehe, C. E. (2020). Deep support vector machine for hyperspectral image classification. Pattern Recognition, 103. https://doi.org/10.1016/j.patcog.2020.107298
  40. Naushad, R., Kaur, T., & Ghaderpour, E. (2021). Deep transfer learning for land use and land cover classification: A comparative study. Sensors, 21(23). https://doi.org/10.3390/s21238083
  41. Zhang, X., Han, L., Han, L., & Zhu, L. (2020). How well do deep learning-based methods for land cover classification and object detection perform on high resolution remote sensing imagery? Remote Sensing, 12(3). https://doi.org/10.3390/rs12030417
  42. Asortse, I., Stewart, J. C., & Davis, G. A. (2024). LAND USE CLASSIFICATION OF SATELLITE IMAGES WITH CONVOLUTIONAL NEURAL NETWORKS (CNNS). Issues In Information Systems. https://doi.org/10.48009/2_iis_2024_122
  43. Guo, R., Zhao, X., Zuo, G., Wang, Y., & Liang, Y. (2023). Polarimetric Synthetic Aperture Radar Image Semantic Segmentation Network with Lovász-Softmax Loss Optimization. Remote Sensing, 15(19). https://doi.org/10.3390/rs15194802