## 1 Introduction  
Unmanned aerial vehicles (UAV) are utilized as a cost-efficient and prompt methodology for taking remote sensing (RS) images. The boon of UAV technology involves least cost, small size, security, natural function, and, especially, the fast and on-demand acquisition of images [1]. The developments of UAV technologies have achieved the state that it can offer intense higher resolution RS images encircling lavish contextual and spatial information. This has allowed studies suggesting numerous original applications for UAV image examination, comprising disaster management, vegetation monitoring, object detection, detection and mapping of archaeological sites, oil and gas pipeline monitoring, and urban site analysis [2; 3]. Aerial image classification methodologies grant distinct semantic categories that are usually established through exploiting changes in spatial deployments and structural forms for designing scenes [4]. In opposition with object or pixel related classifier methods, scene classification provides localization data from extensive aerial image which has apparent semantic data of the surfaces. Such methodologies are classified into three categories, which are: high level vision information, low level visual features, and mid-level visual representations [5; 6]. Aerial scenes are differentiated by low level characteristics use, structural features, texture, spectral, and so on. Subsequently, low level feature vectors are pictorial ascriptions which can be derived globally or locally and are usually utilized for describing aerial scene images [7; 8]. The typical low level feature methods are local binary patterns (LBP), Global Invariant Scale Transform (GIST), and color histogram Scale Invariant Feature Transform (SIFT). Mid-level analytical methods try to advance complete scene illustrations through conveying high order statistical outlines which are created by deriving local visual qualities [9]. The common processing pipeline derives local image patches, and they are programmed as local signals; therefore, creating a complete mid-level depiction of the aerial scenes. The familiar mid-level procedure is Bag of Visual Words (BoVW). Deep learning (DL) procedures like Convolutional Neural Networks (CNNs) were broadly recognized as a notable approach for numerous computer vision applications (classification, image or video recognition, and detection), and have revealed amazing outcomes in various applications [10]. Therefore, there comes numerous advantages to stopping from utilizing DL methods in emergency response and calamity management applications to restore crucial data in a timely manner and permitting superior research and response in the course of time-critical circumstances, and supporting the decision-making processes [11]. Although CNNs were rising successfully at several classification roles via transfer learning (TL), their interpretation speed on implanted platforms, like those discovered on-board UAVs, is hampered by the high computational cost, which may acquire and the model size of these networks is prohibitive from a memory standpoint for these entrenched gadgets [12]. At the same time, most of the earlier works do not consider hyperparameter tuning process into account. This article focuses on the design of swarm intelligence with deep transfer learning driven aerial image classification (SIDTLD-AIC) model on UAV networks. The presented SIDTLD-AIC model follows a feature extraction module using RetinaNet model, in which the hyperparameter optimization process is performed by the use of salp swarm algorithm (SSA). The SSA is chosen as it avoids the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. In addition, a cascaded long short term memory (CLSTM) model is executed for classifying the aerial images. At last, seeker optimization algorithm (SOA) is applied as a hyperparameter optimizer of the CLSTM model and thereby results in enhanced classification accuracy.