Multispectral image analysis for monitoring by IoT based wireless communication using secure locations protocol and classification by deep learning techniques

Abstract

Land surface data is one of the entry points for a enormous resource management and human activity such as to monitor farming, ecology, urban management, and territories. This research aimed to improve the performance of the multispectral image processing technique using a novel deep learning based classification approaches used in classifying the surfaces in multispectral satellite images. The monitoring of Internet of Things (IoT) based wireless communication using secure locations sensor network protocol (SLSNP). The multispectral satellite images are collected based on developing and developed countries. Upon the collection of multispectral image data using deep convolution neural network (DCNN) deep learning system landmarks of the multispectral images are estimated. This will be effective for the identification of resources in a particular location. The simulation results show classification accuracy, precision, recall and the network lifetime, efficiency, throughput based on data transmission from satellite for secure location based wireless sensor networks (WSNs).