Low Complexity Encoder with Multilabel Classification and Image Captioning Model

Due to the advanced development in the multimedia-on-demandtraffic in different forms of audio, video, and images, has extremely movedon the vision of the Internet of Things (IoT) from scalar to Internet ofMultimedia Things (IoMT). Since Unmanned Aerial Vehicles (UAVs) generates a massive quantity of the multimedia data, it becomes a part of IoMT,which are commonly employed in diverse application areas, especially forcapturing remote sensing (RS) images. At the same time, the interpretationof the captured RS image also plays a crucial issue, which can be addressedby the multi-label classification and Computational Linguistics based imagecaptioning techniques. To achieve this, this paper presents an efficient lowcomplexity encoding technique with multi-label classification and image captioning for UAV based RS images. The presented model primarily involves thelow complexity encoder using the Neighborhood Correlation Sequence (NCS)with a burrows wheeler transform (BWT) technique called LCE-BWT forencoding the RS images captured by the UAV. The application of NCS greatlyreduces the computation complexity and requires fewer resources for imagetransmission. Secondly, deep learning (DL) based shallow convolutional neural network for RS image classification (SCNN-RSIC) technique is presentedto determine the multiple class labels of the RS image, shows the novelty ofthe work. Finally, the Computational Linguistics based Bidirectional EncoderRepresentations from Transformers (BERT) technique is applied for imagecaptioning, to provide a proficient textual description of the RS image. Theperformance of the presented technique is tested using the UCM dataset. Thesimulation outcome implied that the presented model has obtained effectivecompression performance, reconstructed image quality, classification results,and image captioning outcome.