Development of Adaptive Time-Weighted Dynamic Time Warping for Time Series Vegetation Classification Using Satellite Images in Solapur District

Abstract

Abstract The global seasonal change and continued rapid growth have maximized the need to assess the urban dwellers’ depend on vegetation for their lives, and also in the urban ecosystem resources. The conventional outcomes devoted to cropland mapping, with the help of high-quality remote sensing data’s. This paper is to investigate and develop a new methodology that pertains to time series analysis for classifying the type of vegetation in a farm area of Ujani Dam located in Solapur District, Maharastra. The proposed model develops a novel adaptive time-weighted dynamic time warping (ATWDTW) for the time series analysis using the satellite images. The gathered satellite images from the farm are processed initially and subjected to analysis by ATWDTW. The TWDTW concept is optimally tuned by the new hybrid meta-heuristic algorithm termed moth flame-based bird swarm optimization (MF-BSA) for enhancing the classification performance. Regarding the false omission rate of the proposed MF-BSA-ATWDTW model attains 5.56% and 29.9% lower than SVM and K-means respectively. From the analysis, it is possible to get a deep insight into the vegetation to be done in each year, and the comparative analysis proves that the proposed model is further adaptable for experimental use in relating and explaining environmental and ecological time-series information.