Advanced Satellite Image Classification of Various Resolution Image Using a Novel Approach of Deep Neural Network Classifier

Abstract

Image registration is computationally intensive and applied in a variety of applications, for example, multispectral classification, change recognition, climate prediction and multi-view analysis in GIS and medicine. There are three types of registration namely multi-view, multimodal and multi-temporal. In multi-view based registration, the images of the same scene taken at different viewpoints are analyzed and modeled for the requirement. Hence stereoscopic image sequences of the same view are acquired, and accurate comparison for the image classification is essential. This paper presents a robust method which has three steps. The first phase includes obtaining hyper spectral (satellite) images and preprocessing of them, the second period subdivides into image blocks for alignment, and the final step focuses on classification based on hyper graph structure using deep learning approach. For processing of satellite images, a new method linear iterative clustering and deep neural network classification are employed. Previous works in remote sensing applications involve training samples and hence prior knowledge of image sets which incurs more computational time. The implementation of this method shows an automatic, achieving better accuracy and dynamic reconfigurable image registration in reduced complexity. The mathematical model used is hidden markov chain model for clustering which provides region-wise feature construction for evaluating region shape and contextual information. The work yields classification accuracy of 94.12% which is far better than past outcomes in this engaged field of research. The execution of the usage is examined, a comparison is additionally influenced regarding false classification ratio, time complexity and clustering accuracy is demonstrated.