پژوهش های سنجش از دور و اطلاعات مکانی

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تشخیص و طبقه‌بندی عوارض در تصاویر ‏Sentinel-2 ‎‏ با استفاده شبکه ‏Resnet

مقالات آماده انتشار

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی نقشه برداری، دانشکده مهندسی عمران آب و محیط زیست، دانشگاه شهید بهشتی، تهران، ایران

چکیده

پیشینه و اهداف: امروزه با توجه به استفاده روز افزون از اطلاعات پوشش و کاربری اراضی در کاربردهای مختلف، کسب این اطلاعات امری ضروری می‌باشد. استفاده از تصاویر سنجش از دوری به عنوان راهکار اصلی کسب این اطلاعات محسوب می‌شود. برای استخراج پوشش و کاربری اراضی از این تصاویر، می‌توان از تکنیک‌های طبقه‌بندی تصاویر بهره برد. با توجه به پتانسیل بالای روش‌های یادگیری عمیق در طبقه بندی تصاویر، این روش‌ها می‌توانند به طور موثری در طبقه‌بندی پوشش و کاربری اراضی استفاده شوند. با این حال، استفاده از این روش‌ها همراه با چالش‌هایی نیز می‌باشد. یکی از مشکلات اصلی استفاده از روش‌های یادگیری عمیق، بیش برازش مدل می‌باشد. از دیگر معضلات اصلی این روش‌ها می‌توان به نیازمند بودن این روش‌ها به تعداد بسیار زیاد داده در مرحله آموزش اشاره نمود. همچنین ناپدید شدن و انفجار گرادیان و انتخاب معماری مناسب از دیگر مشکلات و چالش‌های این روش‌ها برای استخراج پوشش و کاربری اراضی از تصاویر سنجش از دور می‌باشند .
روش‌ها‌: هدف اصلی این پژوهش استفاده از تکنیک‌های مختلف برای رفع این چالش‌ها و رسیدن به دقت‌های بالا در انجام طبقه‌بندی پوشش و کاربری اراضی می‌باشد. برای مرتفع نمودن چالش بیش برازش مدل، از تکنیک‌های حذف تصادفی و توقف زودهنگام استفاده شد تا دقت در داده‌های آموزشی و تست نزدیک به یکدیگر باشند. استفاده از روش داده افزایی می‌تواند کمبود داده‌های آموزشی را برطرف نماید و از بیش برازش مدل نیز جلوگیری کند. به همین علت از این روش برای افزایش داده های آموزشی مدل استفاده شد. تکنیک برش گرادیان نیز در این پژوهش استفاده شد تا از انفجار و ناپدید شدن گرادیان در مدل‌های یادگیری عمیق جلوگیری کند. معماری استفاده شده در این پژوهش برای طبقه بندی مجموعه داده EuroSat، مدل ResNet18 بوده است.
یافته‌ها: در ابتدا از این معماری به همراه تکنیک توقف زودهنگام برای انجام طبقه بندی استفاده شد و مدل به دقت کلی 19/91 درصد و ضریب کاپای 9018/0 رسید. سپس به همین مدل تکنیک داده افزایی اضافه شد و مدل به دقت کلی 78/91 درصد و ضریب کاپای 9085/0 دست یافت که نشان می‌دهد نسبت به مرحله قبلی دقت‌های  بهتری حاصل شده است. در مرحله آخر تکنیک حذف تصادفی با نرخ 5/0، برش گرادیان با حدآستانه 1/0 نیز به مدل قبلی اضافه شد و مدل به دقت کلی 11/93 درصد و ضریب کاپای 9233/0 رسید که نسبت به دو مرحله قبلی به دقت های بهتری رسیده است.
نتیجه‌گیری: این نتایج نشان می‌دهد که دقت طبقه‌بندی پوشش و کاربری اراضی مجموعه داده EuroSat در مرحله آخر نسبت به مراحل قبلی به دقت بهتری دست یافته است. 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Object detection and classification with Sentinel-2 imagery using ResNet

نویسندگان [English]

  • P. Heidari
  • A. Milan
  • A.R. Gharagozlou

Department of Surveying Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, ‎Tehran, Iran

چکیده [English]

Background and Objectives: Nowadays, getting land cover and land use information is crucial due to the growing number of uses for this data. The primary method for obtaining this information is considered to be through the utilization of remote sensing images. Image classification techniques should be employed so as to extract land cover and use from these images. Deep learning techniques can be utilized effectively to the classification of land cover and land use simply because of their great potential in image classification. But there are also challenges when applying these techniques as well. Model overfitting is one of the most common issues when utilizing deep learning algorithms. Another major issue with these methods is that they demand a significant amount of data during the training stage. Additionally, gradient exploding/vanishing and determining the suitable architecture are further challenges associated with these methods for extracting land cover and use from remote sensing imagery.
Methods: The main objective of this research is to employ different techniques to overcome the challenges to achieve high classification accuracy. To solve the problem of model overfitting, dropout and early stopping approaches were utilized to ensure that the accuracy of the training and test data were close. The data augmentation strategy can prevent model overfitting in addition to addressing the lack of training data. As a result, this method was employed to augment training data and also avoiding model overfitting. The gradient clipping strategy was additionally used in this study to mitigate gradient exploding and vanishings in deep learning models. This study used the ResNet18 model to classify the EuroSat dataset, enabling us to obtain highly effective classification accuracy.
Findings: Initially, this architecture was used with with the early stopping strategy, and the model had an overall accuracy of 91.19 percent and a kappa coefficient of 0.9018. The data augmentation technique was then applied to the same model, and the model achieved an overall accuracy of 91.78 percent with a kappa coefficient of 0.9085, surpassing the previous stage. In the last stage, a dropout method with a rate of 0.5 and a gradient clipping with a threshold of 0.1 were added to the previous model, and the model achieved an overall accuracy of 93.11 percent and a kappa coefficient of 0.9233, which was more accurate than the previous two stages.
Conclusion: These results indicate that the EuroSat's land cover and land use classification accuracy in the final stage was higher than in prior stages.

کلیدواژه‌ها [English]

  • Classification
  • Land Cover and Land Use
  • Remote Sensing
  • Deep Learning
  • Convolutional Neural Network

COPYRIGHTS

© 2025 The Author(s).  This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

(https://creativecommons.org/licenses/by-nc/4.0/)

مراجع
[1] Wang, H., Zhao, X., Zhang, X., Wu, D., & Du, X. (2019). Long Time Series Land Cover Classification in China from 1982 to 2015 Based on Bi-LSTM Deep Learning. Remote Sensing, 11(14), 1639.
https://doi.org/10.3390/rs11141639
[2] Zhang, J., & Zhang, Y. (2007). Remote sensing research issues of the National Land Use Change Program of China. ISPRS Journal of Photogrammetry and Remote Sensing, 62(6), 461–472. https://doi.org/10.1016/j.isprsjprs.2007.07.002
[3] Cheruto, M.C., Kauti, M.K., Kisangau, P.D. and Kariuki, P. (2016) Assessment of Land Use and Land Cover Change Using GIS and Remote Sensing Techniques: A Case Study of Makueni County, Kenya. Journal of Remote Sensing and GIS, 5, 175.
DOI: 10.4175/2469-4134.1000175
[4] Mu, L., Wang, L., Wang, Y., Chen, X., & Han, W. (2019). Urban Land Use and Land Cover Change Prediction via Self-Adaptive Cellular Based Deep Learning with Multisource Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12), 5233–5247.
DOI: 10.1109/JSTARS.2019.2956318
[5] 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), 417.
https://doi.org/10.3390/rs12030417
[6] Zhang, C., Harrison, P. A., Pan, X., Li, H., Sargent, I., & Atkinson, P. M. (2020). Scale Sequence Joint Deep Learning (SS-JDL) for land use and land cover classification. Remote Sensing of Environment, 237, 111593.
https://doi.org/10.1016/j.rse.2019.111593
[7] Li, C., & Shao, G. (2012). Object-oriented classification of land use/cover using digital aerial orthophotography. International journal of remote sensing, 33(4), 922-938.
https://doi.org/10.1080/01431161.2010.536183
[8] Mustafa Atasoy, Fevzi Karslı, Cemal Bıyık, & Demir, O. (2006). Determining Land Use Changes with Digital Photogrammetric Techniques. Environmental Engineering Science, 23(4), 712–721. https://doi.org/10.1089/ees.2006.23.712
[9] Mondal, Md., Sharma, N., Kappas, M., & Garg, P. (2015). Critical Assessment of Land Use Land Cover Dynamics Using Multi-Temporal Satellite Images. Environments, 2(4), 61–90.
https://doi.org/10.3390/environments2010061
[10] Luo, M., & Ji, S. (2022). Cross-spatiotemporal land-cover classification from VHR remote sensing images with deep learning-based domain adaptation. ISPRS Journal of Photogrammetry and Remote Sensing, 191, 105-128.
https://doi.org/10.1016/j.isprsjprs.2022.07.011
[11] Lee, S.-H., Han, K.-J., Lee, K., Lee, K.-J., Oh, K.-Y., & Lee, M.-J. (2020). Classification of Landscape Affected by Deforestation Using High-Resolution Remote Sensing Data and Deep-Learning Techniques. Remote Sensing, 12(20), 3372.
https://doi.org/10.3390/rs12203372
[12] Li, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., & Clinton, N. (2016). Stacked Autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. International Journal of Remote Sensing, 37(23), 5632–5646. https://doi.org/10.1080/01431161.2016.1246775
[13] Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS journal of photogrammetry and remote sensing, 152, 166-177.
https://doi.org/10.1016/j.isprsjprs.2019.04.015
[14] Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716.
https://doi.org/10.1016/j.rse.2020.111716
[15] Huang, B., Zhao, B., & Song, Y. (2018). Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sensing of Environment, 214, 73–86.
https://doi.org/10.1016/j.rse.2018.04.050
[16] Digra, M., Dhir, R., & Sharma, N. (2022). Land use land cover classification of remote sensing images based on the deep learning approaches: a statistical analysis and review. Arabian Journal of Geosciences, 15(10).
https://doi.org/10.1007/s12517-022-10246-8
[17] Tong, X.-Y., Xia, G.-S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2020). Land-cover classification with high-resolution remote sensing images using transferable deep models. Remote Sensing of Environment, 237, 111322.
https://doi.org/10.1016/j.rse.2019.111322
[18] Tong, X. Y., Xia, G. S., Lu, Q., Shen, H., Li, S., You, S., & Zhang, L. (2018). Learning transferable deep models for land-use classification with high-resolution remote sensing images. arXiv preprint arXiv:1807.05713. DOI:10.48550/arXiv.1807.05713
[19] Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., & Zhang, S. (2018). Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors, 18(11), 3717.
https://doi.org/10.3390/s18113717
[20] Zhao, J., Wang, L., Yang, H., Wu, P., Wang, B., Pan, C., & Wu, Y. (2022). A Land Cover Classification Method for High-Resolution Remote Sensing Images Based on NDVI Deep Learning Fusion Network. Remote Sensing, 14(21), 5455.
https://doi.org/10.3390/rs14215455
[21] Zhang, L., Zhang, L., & Du, B. (2016). Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art. IEEE Geoscience and Remote Sensing Magazine, 4(2), 22–40. DOI: 10.1109/MGRS.2016.2540798
[22] Cheng, X., He, X., Qiao, M., Li, P., Hu, S., Chang, P., & Tian, Z. (2022). Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images. International Journal of Applied Earth Observation and Geoinformation, 107, 102706–102706.
https://doi.org/10.1016/j.jag.2022.102706
[23] Vali, A., Comai, S., & Matteucci, M. (2020). Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P., & Zhang, S. (2018). Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery. Sensors, 18(11), 3717. https://doi.org/10.3390/s18113717
[24] Carranza-García, M., García-Gutiérrez, J., & Riquelme, J. C. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sensing, 11(3), 274. https://doi.org/10.3390/rs11030274
[25] Rehmer, A., & Kroll, A. (2020). On the vanishing and exploding gradient problem in Gated Recurrent Units. IFAC-PapersOnLine, 53(2), 1243–1248.
https://doi.org/10.1016/j.ifacol.2020.12.1342
[26] Simon, J. B., Karkada, D., Ghosh, N., & Belkin, M. (2023). More is better in modern machine learning: when infinite overparameterization is optimal and overfitting is obligatory. arXiv preprint arXiv:2311.14646.
https://doi.org/10.48550/arXiv.2311.14646
[27] Li, Y., Zhang, H., Xue, X., Jiang, Y., & Shen, Q. (2018). Deep learning for remote sensing image classification: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(6). https://doi.org/10.1002/widm.1264
[28] Marschner, F. J., & Anderson, J. R. (1967). Major land uses in the United States. US Geological Survey.
DOI: 10.3133/70046790
[29] Vali, A., Comai, S., & Matteucci, M. (2020). Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review. Remote Sensing, 12(15), 2495.
https://doi.org/10.3390/rs12152495
[30] Jozdani, S. E., Johnson, B. A., & Chen, D. (2019). Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification. Remote Sensing, 11(14), 1713.
https://doi.org/10.3390/rs11141713
[31] Ali, K., & Johnson, B. A. (2022). Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach. Sensors, 22(22), 8750. https://doi.org/10.3390/s22228750
[32] Li, W., Wang, Z., Wang, Y., Wu, J., Wang, J., Jia, Y., & Gui, G. (2020). Classification of High-Spatial-Resolution Remote Sensing Scenes Method Using Transfer Learning and Deep Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 1986–1995.
DOI: 10.1109/JSTARS.2020.2988477
[33] Yu, X., Wu, X., Luo, C., & Ren, P. (2017). Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework. GIScience & Remote Sensing, 54(5), 741–758.
https://doi.org/10.1080/15481603.2017.1323377
[34] Shawky, O. A., Hagag, A., El-Dahshan, E.-S. A., & Ismail, M. A. (2020). Remote sensing image scene classification using CNN-MLP with data augmentation. Optik, 221, 165356.
https://doi.org/10.1016/j.ijleo.2020.165356
[35] Zhang, Z., Mi, X., Yang, J., Wei, X., Liu, Y., Yan, J., Liu, P., Gu, X., & Yu, T. (2023). Remote Sensing Image Scene Classification in Hybrid Classical–Quantum Transferring CNN with Small Samples. Sensors, 23(18), 8010.
https://doi.org/10.3390/s23188010
[36] Alem, A., & Kumar, S. (2022). Transfer learning models for land cover and land use classification in remote sensing image. Applied Artificial Intelligence, 36(1), 2014192.
https://doi.org/10.1080/08839514.2021.2014192
[37] Khan, S. D., & Basalamah, S. (2023). Multi-Branch Deep Learning Framework for Land Scene Classification in Satellite Imagery. Remote Sensing, 15(13), 3408.
https://doi.org/10.3390/rs15133408
[38] Stivaktakis, R., Tsagkatakis, G., & Tsakalides, P. (2019). Deep Learning for Multilabel Land Cover Scene Categorization Using Data Augmentation. IEEE Geoscience and Remote Sensing Letters, 16(7), 1031–1035. DOI: 10.1109/LGRS.2019.2893306
[39] Naushad, R., Kaur, T., & Ghaderpour, E. (2021). Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study. Sensors, 21(23), 8083.
https://doi.org/10.3390/s21238083
[40] Rangel, A., Terven, J., Cordova-Esparza, D. M., & Chavez-Urbiola, E. A. (2024). Land Cover Image Classification. arXiv preprint arXiv:2401.09607.  
https://doi.org/10.48550/arXiv.2401.09607
[41] Zhang, W., Tang, P., & Zhao, L. (2019). Remote Sensing Image Scene Classification Using CNN-CapsNet. Remote Sensing, 11(5), 494. https://doi.org/10.3390/rs11050494
[42] Fayaz, M., Nam, J., Dang, L. M., Song, H.-K., & Moon, H. (2024). Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery. Applied Sciences, 14(5), 1844. https://doi.org/10.3390/app14051844
[43] Albarakati, H .M, Khan, M. A., Hamza, A., Khan, F., Naoufel, K., Jamel, L., Almuqren, L., & Alroobaea, R. (2024). A Novel Deep Learning Architecture for Agriculture Land Cover and Land Use Classification from Remote Sensing Images Based on Network-Level Fusion of Self-Attention Architecture. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1–16. DOI: 10.1109/JSTARS.2024.3369950
[44] Hatef Dastour, & Hassan, Q. K. (2023). A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification. Sustainability, 15(10), 7854–7854.
https://doi.org/10.3390/su15107854
[45] Pedrayes, O. D., Lema, D. G., García, D. F., Rubén Usamentiaga, & Alonso, Á. (2021). Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery. Remote Sensing, 13(12), 2292–2292. https://doi.org/10.3390/rs13122292
[46] Cheng, G., Xie, X., Han, J., Guo, L., & Xia, G. S. (2020). Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3735-3756.
DOI: 10.1109/JSTARS.2020.3005403
[47] Rousset, G., Despinoy, M., Schindler, K., & Mangeas, M. (2021). Assessment of deep learning techniques for land use land cover classification in southern New Caledonia. Remote Sensing, 13(12), 2257. https://doi.org/10.3390/rs13122257
[48] Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019). Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2217-2226. DOI: 10.1109/JSTARS.2019.2918242
[49] Cheng, X., Li, B., Deng, Y., Tang, J., Shi, Y., & Zhao, J. (2024). MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model. Applied Sciences, 14(6), 2226.
https://doi.org/10.3390/app14062226
[50] Stoimchev, M., Kocev, D., & Džeroski, S. (2023). Deep Network Architectures as Feature Extractors for Multi-Label Classification of Remote Sensing Images. Remote Sensing, 15(2), 538. https://doi.org/10.3390/rs15020538
[51] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.48550/arxXiv.1512.03385
[52] Wang, L., Wang, J., Liu, Z., Zhu, J., & Qin, F. (2022). Evaluation of a deep-learning model for multispectral remote sensing of land use and crop classification. The Crop Journal, 10(5), 1435-1451. https://doi.org/10.1016/j.cj.2022.01.009
[53] Hosseiny, B., Abdi, A. M., & Jamali, S. (2022). Urban land use and land cover classification with interpretable machine learning – A case study using Sentinel-2 and auxiliary data. Remote Sensing Applications: Society and Environment, 28, 100843. https://doi.org/10.1016/j.rsase.2022.100843
[54] Shehab, L. H., Fahmy, O. M., Gasser, S. M., & El-Mahallawy, M. S. (2021). An efficient brain tumor image segmentation based on deep residual networks (ResNets). Journal of King Saud University - Engineering Sciences, 33(6), 404–412. https://doi.org/10.1016/j.jksues.2020.06.001
[55] Wang, Y., Li, K., Xu, L., Qian, W., Wang, F., & Chen, Y. (2021). A Depthwise Separable Fully Convolutional ResNet with ConvCRF for Semisupervised Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4621–4632.
DOI: 10.1109/JSTARS.2021.3073661
[56] Safari, M. M., Sharifi, A., Mahmoodi, J., & Dariush Abbasi-Moghadam. (2024). Mesoscale eddy detection and classification from sea surface temperature maps with deep neural networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 10279–10290.
DOI: 10.1109/JSTARS.2024.3402823
[57] Nava, L., Monserrat, O., & Catani, F. (2022). Improving Landslide Detection on SAR Data Through Deep Learning. IEEE Geoscience and Remote Sensing Letters, 19, 1–5.
DOI: 10.1109/LGRS.2021.3127073
[58] Bera, S., & Shrivastava, V. K. (2020). Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification. International Journal of Remote Sensing, 41(7), 2664-2683. https://doi.org/10.1080/01431161.2019.1694725
[59] Truong, T. X., Nhu, V.-H., Phuong, D. T. N., Nghi, L. T., Hung, N. N., Hoa, P. V., & Bui, D. T. (2023). A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas. Remote Sensing, 15(14), 3458.
https://doi.org/10.3390/rs15143458
[60] Reyad, M., Sarhan, A. M., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35(23), 17095-17112.
https://doi.org/10.1007/s00521-023-08568-z
[61] Macarringue, L. S., Bolfe, É. L., & Pereira, P. R. M. (2022). Developments in Land Use and Land Cover Classification Techniques in Remote Sensing: A Review. Journal of Geographic Information System, 14(01), 1–28.
DOI: 10.4236/jgis.2022.141001
[62] Abdu, H. A. (2018). Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images. International Journal of Remote Sensing, 40(4), 1275–1300.
https://doi.org/10.1080/01431161.2018.1524587
[63] HAY, A. M. (1988). The derivation of global estimates from a confusion matrix. International Journal of Remote Sensing, 9(8), 1395–1398.
https://doi.org/10.1080/01431168808954945
[64] Layati, E., & El Ghachi, M. (2024). Oued Lakhdar watershed (Morocco), monitoring land use/cover changes: Remote sensing and GIS approach. Geology, Ecology, and Landscapes, 1-13. https://doi.org/10.1080/24749508.2024.2395204
[65] Fan, F., Wang, Y., & Wang, Z. (2007). Temporal and spatial change detecting (1998–2003) and predicting of land use and land cover in Core corridor of Pearl River Delta (China) by using TM and ETM+ images. Environmental Monitoring and Assessment, 137(1-3), 127–147.
https://doi.org/10.1007/s10661-007-9734-y
[66] Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.
https://doi.org/10.1016/0034-4257(91)90048-B
[67] Rwanga, S. S., & Ndambuki, J. M. (2017). Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS. International Journal of Geosciences, 08(04), 611–622. DOI: 10.4236/ijg.2017.84033
[68] Zhou, Y., Zhang, R., Wang, S., & Wang, F. (2018). Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy. Sensors, 18(7), 2013.
https://doi.org/10.3390/s18072013
[69] Temenos, A., Temenos, N., Kaselimi, M., Doulamis, A., & Doulamis, N. (2023). Interpretable Deep Learning Framework for Land Use and Land Cover Classification in Remote Sensing Using SHAP. IEEE Geoscience and Remote Sensing Letters, 20, 1–5. DOI: 10.1109/LGRS.2023.3251652
[70] Moradi, R., Berangi, R., & Minaei, B. (2019). A survey of regularization strategies for deep models. Artificial Intelligence Review, 53(6), 3947–3986.
https://doi.org/10.1007/s10462-019-09784-7
[71] Salehin, I., & Kang, D.-K. (2023). A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain. Electronics, 12(14), 3106.
https://doi.org/10.3390/electronics12143106
[72] Piotrowski, A. P., Napiorkowski, J. J., & Piotrowska, A. E. (2020). Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling. Earth-Science Reviews, 201, 103076.
https://doi.org/10.1016/j.earscirev.2019.103076
[73] Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48. https://doi.org/10.1186/s40537-019-0197-0
[74] Hao, X., Liu, L., Yang, R., Yin, L., Zhang, L., & Li, X. (2023). A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition. Remote Sensing, 15(3), 827. https://doi.org/10.3390/rs15030827
[75] Ceni, A. (2022). Random orthogonal additive filters: a solution to the vanishing/exploding gradient of deep neural networks. arXiv preprint arXiv:2210.01245.
DOI: 10.1109/TNNLS.2025.3538924
[76] Li, P., Han, L., Tao, X., Zhang, X., Grecos, C., Plaza, A., & Ren, P. (2020). Hashing Nets for Hashing: A Quantized Deep Learning to Hash Framework for Remote Sensing Image Retrieval. IEEE Transactions on Geoscience and Remote Sensing, 58(10), 7331–7345. DOI: 10.1109/TGRS.2020.2981997
[77] Lei, S., Shi, Z., & Zou, Z. (2017). Super-Resolution for Remote Sensing Images via Local–Global Combined Network. IEEE Geoscience and Remote Sensing Letters, 14(8), 1243–1247.
DOI: 10.1109/LGRS.2017.2704122
[78] Anam, M. K., Defit, S., Haviluddin, H., Efrizoni, L., & Firdaus, M. B. (2024). Early Stopping on CNN-LSTM Development to Improve Classification Performance. Journal of Applied Data Sciences, 5(3), 1175-1188.
https://doi.org/10.47738/jads.v5i3.312
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