A Comparison of Pixel Based and Object Based Image Classification for Cropland Area Estimation

Abstract

Pixel-based (PB) and object-based (OB) Land Use–Land Cover (LULC) classification techniques can be applied in Google Earth Engine (GEE), a flexible cloud-based platform, because of its numerous state-of-the-art functions that comprise several Machine Learning (ML) methods. Adding some texture measure, any measure to a classification usually improves the accuracy. Object segmentation and object textural analysis are two OB methods that are still uncommon in the GEE environment. Object based image classification is difficult because it can be challenging to concatenate the correct functions and adjust various parameters in order to get past the computational limitations of GEE. The goal of this work is to develop and test an OB classification approach that combines the ML algorithm Random Forest (RF) to perform the final classification, the Gray-Level Co-occurrence Matrix (GLCM) to calculate cluster textural indices and the Simple Non-Iterative Clustering (SNIC) algorithm to identify spatial clusters. The primary seven GLCM indices are subjected to a Principal Components Analysis (PCA) in order to combine the textural data needed for the OB classification into a single band. The proposed methodology was broadly tested in a 304 km2 study area, located in the Telangana state (India), using Sentinel 2 (S2).