Integration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery

The problem of extracting the building from mono optical aerial imagery with high spatial resolution is always considered as an important challenge to prepare the maps. The goal of the current research is to take advantage of the semantic segmentation of mono optical aerial imagery to extract the building which is realized based on the combination of deep convolutional neural networks (DCNN) and bilateral filters (BF). For this purpose, considering the hardware limitations of the current research and the fact that it is necessary to select a large number of training data to train deep convolutional neural networks, after selecting an appropriate dataset from three-band optical images, the minimum data that obtains the highest training accuracy was selected to avoid getting weak results due to the lack of training data. In this research, by optimizing the SegNet deep neural network which is an encoder-decoder network, the processing task and therefore extracting the building from optical images are done using the adaptive moment estimation (ADAM) optimization and BF with a Gaussian kernel. This method is implemented on a dataset related to the mono optical aerial imagery of urban regions located in Potsdam, Germany, the two-dimensional tagged datasets of international society for photogrammetry and remote sensing (ISPRS). The results show that compared to similar methods, the combinational use of the SegNet optimized deep neural network and BF with a Gaussian kernel provides very appropriate capabilities to improve the detection of building boundary in the optical images with high spatial resolution. Also, the results of the proposed method show that the values of the integrity and validity criteria are 95.14 and 92.37 respectively for the test area 1, 91.67 and 90.2 respectively for the test area 2, and 96.14 and 93.98 respectively for the test area 3.