LULC Change Detection Using Combined Machine and Deep Learning Classifiers

In this paper we shall describe a method for land use and land cover (LULC) change detection in multi-date multispectral images using a well known post-classification comparison (PCC) principle. The novelty of the proposed method is the use of a random forest (RF) classifier and a deep neural network (DNN) classifier to obtain the multi-temporal land use maps required to perform a PCC process. Recall that RF is a powerful and versatile supervised machine learning algorithm, and DNN is considered as the most basic and effective deep learning architecture. A mono-date multi-classifiers Bayesian decision fusion is executed to enhance the input LULC mapping of the PCC scheme. Co-registered bi-temporal multispectral images taken over the north eastern region of Algiers, Algeria, between 1997 and 2001 by the American LANDSAT-TM satellite are used to verify the effectiveness of the proposed method which is also compared to the more elaborated convolutional neural network (CNN) method. While recently developed deep learning change detection methods (DLCDM) necessitate large labeled training data and place high demands on computational power in hardware and software, the PCC scheme we proposed is simple to implement, does not consume a lot of computation time and produces results that are consistent with the ground truth of the study zone.