A Comparative Review on Pixel-Based and Object-Based Approach for Land Cover (LC) Classification

Image classification is one of the most basic techniques of digital image processing. This review focuses on the strengths and weaknesses of traditional pixel-based classification and object-based classification algorithms for the extraction of information from remotely sensed imageries. Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, and vegetation condition study and soil management. The LULC classification remains a difficult task and it is especially challenging in heterogeneous season landscapes where such maps are of great importance. Over the last decade, a number of classification algorithms have been developed for the analysis of remotely sensed data. The most algorithms are the pixel-based classification and object-oriented classification K-Nearest Neighbours (K-NN), Support Vector Machines (SVMs), the Decision Trees (DTs) and maximum likelihood classification (MLC) etc. Generally, classifiers information extraction can be divided into three categories: a] based on the type of learning (supervised and unsupervised), b] based on assumptions on data distribution (parametric and non-parametric) and, c] based on the number of outputs for each spatial unit (hard and soft). In this research, a comparative pixel-based and object-based land cover classification was developed in which advantages and disadvantages depending upon their area of application of both pixels and objects were different. This approach makes use of both pixel and object spectral features resulting from image segmentation through a comparative mechanism to resolve the problem of spectral confusion caused by reflectance similarity of some land cover types that traditional pixel-based classification cannot resolve.