Classification of Multi-temporal Images using Machine Learning

Abstract

In past, there has been a lot of research related to the image-based technique in remote sensing from which object-based classification is giving great results among all the techniques. This paper presents a new approach where we have mixed both OBIA (Object-Based Image Analysis) & supervised classification. And with this novel approach, our team aims to do classification as well as analysis for the change detection over time. The data used in this study is high-resolution Multispectral 4-band images from 2017 to 2019 (i.e. 3.0 m) provided by the PlanetScope satellite of region Chandigarh, India. Here the data has been pre-processed through passing it in a pipeline of steps and used a Multi-resolution segmentation algorithm and classify the 7 classes through supervised learning using 3 algorithms Maximum Likelihood (ML), Support Vector Machine (SVM), Mahalanobis Distance (MD). And out of the three, SVM and ML has given the highest Overall Accuracy of 95.21% & Kappa Coefficient = 0.9159 and Overall Accuracy 91.91% & Kappa Coefficient = 0.8860. Altogether; this is a highly effective approach for classification and detecting the change in Urban area or Rural area or forest area than simply using OBIA or pixel-based approach.