Multi-class Object Detection Model in Satellite Images Using Convolutional Neural Network

Abstract

The accurate multi-detection of objects in satellite images has become very essential due to the high criminal activities that posed security threat to humanity all over the world. However, there are significant limitations of traditional methods of multi-object detection such as matching based techniques and object based image analysis. Although Convolutional neural network and image processing techniques has been proved to be essential fields in so many applications of computer vision specifically multi-object detection, multi-object classification, object retrieval, object recognition and object segmentation in a digital image or video, however, multi-object detection especially in satellite images suffer from problems such as shadow, camouflage, and occlusion. The aim of this research work was to design a robust multi-class object detection model in satellite images using image processing techniques and convolutional neural network with a particular concern on image preprocessing, image denoising and image enhancement to enable address the issue of noise in satellite images. The Satellite image that are propose for this model is LandSat-8, because it is free access for research and have a tract record in terms of consistency. The proposed model applied supervised learning algorithm for training different samples of labeled data for the model to enable the system detect vegetation, water bodies, road networks and building. This research will enable the government to know the positions as well as the coordinates of every thick forest, drainage, road networks and buildings in the forest for security reasons. It is at the heart of this research to pave away for the full implementation of this model using either MATLAB or Python Programming.