Document Type : Original Article
Authors
1
Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran, Iran
2
Department of Geomatics Engineering, ST.C., Islamic Azad University, Tehran, Iran
10.22059/eoge.2025.396331.1179
Abstract
One of the primary applications of remote sensing is the classification of land use and land cover (LULC). This field has increased prominence in computer vision tasks with the expansion of learning methods, especially in recent years. In this context, the present study introduces a convolutional neural network architecture named HCNN, designed to achieve high accuracy in LULC classification while minimizing processing cost. A simpler architecture based on conventional CNN designs, referred to as SCNN, was also implemented for comparison. The HCNN architecture comprises residual, dense, inception, and squeeze-excitation blocks, along with several base layers. Four models based on these two architectures were trained using RGB and multispectral data from the Sentinel-2 imagery in the EuroSAT dataset: SCNN-RGB, SCNN-MS, HCNN-RGB, and HCNN-MS. All models achieved accuracies above 94%. Among these, HCNN-MS attained the highest accuracy of 98.44%, while SCNN-RGB recorded the lowest accuracy at 95.57%. Overall, HCNN-based models outperformed SCNN-based models in accuracy and training speed, requiring approximately six times less training time. Additionally, the use of multispectral data had a positive impact on model accuracy, albeit it increased computational complexity somewhat. An ablation study was also conducted to evaluate the role of each block in the HCNN architecture on the model’s final performance. The ablation results demonstrated that each block plays a significant role in improving accuracy and reducing processing overhead, particularly the residual and dense blocks, which had the greatest impact on final accuracy. Moreover, the squeeze-excitation block notably reduced training time, while its removal caused minimal change in accuracy.
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