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- MakaleAçık Erişim2025Scopus SJRQ2Optimizing Land Image Classification: A Deep Learning Approach with ResNet-101 on the EuroSAT Dataset
Celik, Fatih; Celik, Kemal
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, F1score, 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.
AtıfWoS4OpenAlex3İncele - MakaleAçık Erişim2025Scopus SJRQ3Lightweight Transformer Model for Agricultural Land Use and Land Cover Classification
Celik, Kemal
Observing agricultural land use via remote sensing images is essential for ensuring food security, estimating yields and planning efficient exports nonetheless precise classification continues to be difficult because of the varied and evolving characteristics of agricultural environments. This research aims to evaluate and optimize advanced deep learning architectures particularly Vision Transformer (ViT) models for agricultural land-use classification tasks. Specifically, we employed ViT-Base-16 and other lightweight models DeiT-Tiny and EfficientNet-B0 applying techniques such as model layer compression and advanced data augmentation CutMix and Cutout to achieve high accuracy while significantly reducing computational complexity. Evaluation was performed using three benchmark remote sensing datasets EuroSAT, NWPU-RESISC45 and SIRI-WHU which include diverse spatial resolutions and agricultural classes relevant for practical monitoring. Findings indicate that the optimized ViT algorithm is highly effective in recognizing global spatial connections, consistently achieving remarkable classification accuracy exceeding 99% on a newly assembled dataset containing around 200 samples of Google Earth imagery. Furthermore, for the first time in agricultural image classification compressing the ViT-Base model by pruning 50% of its layers significantly reduced complexity maintaining competitive accuracy 97.9% on SIRI-WHU. The resulting models are particularly suitable for deployment on devices with limited computational resources supporting real-world operational agricultural monitoring systems. This study emphasizes the revolutionary possibilities and practical use of optimized transformer-based models that offer scalable and efficient solutions specifically designed for precision agriculture applications.
- MakaleAçık Erişim2024Scopus SJRQ1A study on effective data preprocessing and augmentation method in diabetic retinopathy classification using pre-trained deep learning approaches
İncir, Ramazan; Bozkurt, Ferhat
High glucose levels in the blood not only damage different tissues and organs of the body, but also cause adverse effects on the eye. This condition is called diabetic retinopathy (DR). DR can cause blurred vision, darkening of the field of vision, and severe vision loss. The number of people infected with the disease is increasing in our country and worldwide. The time-consuming physician check-ups and the presence of small lesions indicate the need to develop diagnostic systems. Deep learning-based applications have become the trend for diagnosing and grading diseases from images. This study aims to create a meaningful and sufficient dataset using effective data preprocessing and affine transformation techniques in diabetic retinopathy classification. In this study, classification was performed using seven different pre-trained deep learning architectures. An experimental study of each technique was performed on the EyePACS dataset. An overfitting problem was encountered in the experimental results with the original data set. Thus, data preprocessing and data augmentation processes were carried out in order to eliminate overfitting by considering the imbalance between classes in the dataset. The classification performance obtained from each architecture was observed according to performance metrics of precision, recall, F1 Score, accuracy, and loss. In this study, the best performance was achieved with 97.65% test accuracy with the proposed EfficientNetV2-M network model.
AtıfScopus32WoS21OpenAlex34İncele