Leveraging High Resolution Remote Sensing Images for Vehicle Classification using Sea Lion Optimization with Deep Learning Model

High-resolution remote sensing data renders worldwide images faster than traditional data collection strategies. Hence small objects such as cars are easily detected. Vehicle recognition on aerial remote sensing images (RSIs) in the complicated background of urban zones has always gained a lot of interest in the remote sensing field. The automatic vehicles enumeration research domain plays a significant role in several applications, including traffic management and monitoring. Target detection technology will be a crucial part of computer vision (CV) technology, and target detection techniques were enforced in several domains. Therefore, this study develops a new Vehicle Recognition and Classification using Sea Lion Optimization with Deep Learning (VRC-SLODL) model on RSI. In the presented VRC-SLODL technique, the major intention lies in recognising and classifying vehicles present in the images. The bilateral filtering (BF) technique can initially improve the RSI quality. The VRC-SLODL technique employs a modified residual network (ResNet) model to produce a set of feature vectors. Finally, the SLO algorithm with long short-term memory (LSTM) model is employed for vehicle classification, where the SLO algorithm acts as a hyperparameter optimizer. The experiments were performed on a benchmark dataset to examine the better performance of the VRC-SLODL technique. The obtained values reported the improved classification performance of the VRC-SLODL technique over other models.