Automatic building change image quality assessment in high resolution remote sensing based on deep learning

Abstract The multi-temporal high-resolution remote sensing (HRRS) images are usually acquired at different imaging angles, with serious noise interferences and obvious building shadows, so that detecting the changes of urban buildings is a problem. In order to address this challenge, a deep learning-based algorithm called ABCDHIDL is proposed to automatically detect the building changes from multi-temporal HRRS images. Firstly, an automatic selection method of labeled samples of building changes based on morphology (ASLSBCM) is proposed. Secondly, a deep learning model (DBN-ELM) for building changes detection based on deep belief network (DBN) and extreme learning machine (ELM) is proposed. A convolution operation is employed to extract the spectral, texture and spatial features and generate a combined low-level features vector for each pixel in the multi-temporal HRRS images. The unlabeled samples are introduced to pre-train the DBN, and the parameters of DBN-ELM are globally optimized by jointly using the ELM classifier and the labeled samples are offered by ASLSBCM to further improve the detection accuracy. In order to evaluate the performance of ABCDHIDL, four groups of double-temporal WorldView2 HRRS images in four different experimental regions are selected respectively as the test datasets, and five other representative methods are used and compared with ABCDHIDL in the experiments of buildings change detection. The results show that ABCDHIDL has higher accuracy and automation level than the other five methods despite its relatively higher time consumption.