# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery  
[PERSON]  
1 Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (M.F. ); [EMAIL_ADDRESS] (J.N.) 2 Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea; [EMAIL_ADDRESS] (L.M.D. ); [EMAIL_ADDRESS] (H.-K.S.) [1]  
and multi-source RS images from diverse geographic regions [2] present new opportunities for multiple-time-scale LC classification. Nevertheless, the complex features visible in HRRS images, such as geometrical and object structures, introduce new challenges for classification [3]. Variability in photographic distortions, scale variations, and illumination changes in RS images make it difficult to apply existing models to diverse HRRS images effectively [4]. Furthermore, spectral and spectral-spatial (SS) features have traditionally been employed in the literature to interpret RS images for land-cover classification [5]. However, these features struggle to capture the contextual information in HRRS images due to increased spatial resolution [6]. To address these challenges, deep CNNs have gained attention for their ability to comprehend HRRS images [7] and represent semantic and high-level image properties [8; 9], such as scene classification [10], object detection [11], image retrieval [12], as well as LC classification [13]. However, two significant issues arise when applying deep models to LC classification with multi-source HRRS data: insufficient transferability and the lack of an extensive, well-annotated land-cover dataset. The challenges in applying land-cover classification with multi-source images include issues of insufficient transferability, where models struggle to adapt effectively to diverse dataset images, and the absence of extensive well-annotated land-cover dataset images. To address these issues, therefore, this study introduces an efficient and robust LAC system that harnesses fine-tuned versions of Inception-v3 and DenseNet networks, leveraging transfer learning. The primary objectives include enhancing LC classification performance through the contextual understanding of deep CNNs and conducting a comparative analysis to assess the system's advancements and competitive edge over existing models in the field of satellite sensing imagery interpretation. This approach addresses the limitations posed by the diversity and complexity of HRRS images, ultimately contributing to improved land-cover classification in remote-sensing applications. Various CNN models, including Inception-v [14], DenseNet201 [8], and ResNet-50 [15], have been trained on a labeled dataset. Recent research highlights innovative applications of deep learning, especially (CNNs) in the unmanned aerial vehicle (UAV) domain. For instance, [PERSON], [PERSON], and [PERSON]'s work [16] combines CNNs and cooperative spectrum detection for unauthorized drone identification. [PERSON] et al. [17] employ a stacked auto-encoder deep learning approach to achieve accurate forest area assessment using UAV-captured images applicable to forest management. [PERSON], [PERSON], and [PERSON] et al. 's research [18] leverages CNNs to identify diverse drone types, achieving over 90% accuracy in recognition and showcasing the prowess of CNNs in drone identification across various models and shapes, including radio-controlled flying objects [19]. Recent studies have showcased the diverse applications of deep learning in the fields of image classification and object detection. [PERSON], [PERSON], and [PERSON] [20] introduced a classification method for rotary-wing unmanned aerial vehicles and birds, enhancing image classification accuracy notably with the CDNTS layer.