Enhancing Object Detection and Classification Using White Shark Optimization with Deep Learning on Remote Sensing Images

Abstract

Remote sensing (RS) object detection is extensively applied in the fields of civilian and military. The important role of remote sensing is to identify objects like planes, ships, harbours airports, etc., and then it can attain position information and object classification. It is of considerable importance to use RS images for observing the densely organized and directional objects namely ships and cars parked in harbours and parking areas. The object detection (OD) process involves object localization and classification. Due to its wide coverage and longer shooting distance, Remote sensing images (RSIs) have hundreds of smaller objects and dense scenes. Deep learning (DL), in particular convolution neural network (CNN), has revolutionized OD in different fields. CNN is devised to automatically learn the hierarchical representation of data, which makes them fit for feature extraction. Hence, the study proposes a new white shark optimizer with DL-based object detection and classification on RSI (WSODL-ODCRSI) method. The purpose of the WSODL-ODCRSI model is to classify and detect the presence of the objects in the RSI. To accomplish this, the WSODL-ODCRSI model uses a modified single-shot multi-box detector (MSSD) for the OD process. The next stage of OD is the object classification process, which takes place with the use of the Elman Neural Network (ENN) algorithm. The WSO algorithm is exploited as a parameter-tuning model for improving the object classification results of the ENN approach. The stimulated study of the WSODL-ODCRSI algorithm has been established on the benchmark data set and the outcomes underlined the promising performance of the WSODL-ODCRSI model on the object process of classification