Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Acar, H.(1)
- Akbulut, Z.(1)
- Celik, Fatih(1)
- Celik, Kemal(1)
- Dihkan, M.(1)
- Karsli, F.(1)
- Reis, Catal H.(1)
- Özdemir, S.(1)
Yayınlar
- MakaleAçık Erişim2025Optimizing 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.
- MakaleAçık Erişim2018Automatic extraction of building boundaries from high resolution images with active contour segmentation
Akbulut, Z.; Özdemir, S.; Acar, H.; Dihkan, M.; Karsli, F.
Building extraction from remotely sensed images plays an important role in many applications such as updating geographical information system, change detection, urban planning, disaster management and 3D building modeling. Automatic extraction of buildings from aerial images is not an easy task because of background complexity, lighting conditions and vegetation cover that reduces separability or visibility of buildings. As a result, automatic building extraction can be a complex process for computer vision and image processing techniques. In order to overcome this difficulty region-based active contour model was used to automatically detect the boundary of buildings for this study. To extract object boundaries, the model grows or shrinks the initial contour in the image. The main objective of this paper is making active contours algorithm perform without user interaction and to detect automatically initial contours to segment buildings with a software coded in Matlab. This task carried out by morphological operations, band ratio and thresholding methods. In this study, high resolution aerial images with 8 cm ground sampling distance (GSD) were used. Three separate test zones were selected with varying building level of detail on these images. Finally, it was assessed the accuracy of segmented buildings using Correctness, Completeness and Quality metrics by comparing the results images and manually digitized reference image. The proposed approach for building extraction from images was shown to be 98% accurate on buildings with simple geometry and homogeneous roof textures. However accuracy of extracted buildings with heterogeneous roof textures and lighting, and complex geometry is 89%. The results clearly show that automatically calculated initial contour positions work in accordance with the active contour algorithm and easily extraction of the buildings boundaries.
- MakaleAçık Erişim2018Detection of foot bone anomaly using medical photogrammetry
Reis, Catal H.
Photogrammetry has been used for medical diagnostic and treatment. Mostly used medical photogrammetric techniques are Ultrasound, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) images. CT and MRI are the most effective method for the early detection of foot and ankle anomaly. Researchers have been developing various methods to detect anomaly. Many image segmentation techniques are available in the literature. Computer Aided Diagnosing (CAD) system has been proposed in this study for detection of foot bone anomaly by the analysis of CT images. In this study, a segmentation based on edge detection method is proposed for the classification of anomaly in foot CT images. Edge detection algorithms are the most commonly used techniques in image processing for edge detection. Canny edge detector is evaluated in this study. In this study, .dicom medical image standard format and ten male patient's foot CT images (245 images and 50 test data) are used. The used parameters are detector collimation of 64 mm, scanning thickness of 1-5 mm, and pixel sizes of 512x512 in radiometric resolution of 16 bits' gray levels. The proposed method consists of five major steps: (i) calculating the horizontal & vertical gradient, (ii) determining gradient magnitude and gradient direction, (iii) applying non-maximal suppression, (iv) computing high and low thresholds, (v) hysteresis thresholding are applied to the multi-detector computed tomography to detect the bone anomaly. In this study, automatic edge-based digital image processing techniques are applied to detect of foot bone anomaly. The proposed canny segmentation method enables users segment anomaly in MDCT of foot very quickly and efficiently. The results demonstrate that the proposed segmentation method is effective for segmenting anomaly. The proposed method obtains satisfactory performances in terms of accuracy and F-measure the area under Receiver Operating Characteristic curve (ROC curve (AUC)). The proposed segmentation method achieves an accuracy of 0.86 and F-measure of 0.92, respectively. The purpose of our study is to detect the anomaly of the foot and it was the simplest and less time consuming process.