Extraction of Buildings in Remote Sensing Imagery with Deep Belief Network

In land use analysis, the extraction of buildings from remote sensing imagery is an important problem. This work is difficult to obtain the spectral features from buildings due to high intra-class and low inter-class variation of buildings. In the paper, a patch-based deep belief network (PBDBN) architecture is used for the extraction of buildings from remote sensing datasets. And low-level building features (e.g compacted contours) of adjacent regions are combined with Deep Belief Network (DBN) features during the post-processing stage for obtaining better performance. The experimental results are demonstrated on Massachusetts buildings dataset to express the performance of PBDBN and it is compared with other method on the same dataset.