Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 130; pp. 139 - 149
• Main Authors: Alshehhi, Rasha, Marpu, Prashanth Reddy, Woon, Wei Lee, Mura, Mauro Dalla
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2017; Elsevier
• Subjects: Adjacent regions; asymmetry; buildings; Convolutional neural network; data collection; Engineering Sciences; Extraction; image analysis; land use and land cover maps; Low-level features; neural networks; remote sensing; roads; Signal and Image processing; spatial data; urban areas; Extraction; Adjacent regions; Convolutional neural network; Low-level features
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2017.05.002

Extraction of man-made objects (e.g., roads and buildings) from remotely sensed imagery plays an important role in many urban applications (e.g., urban land use and land cover assessment, updating geographical databases, change detection, etc). This task is normally difficult due to complex data in the form of heterogeneous appearance with large intra-class and lower inter-class variations. In this work, we propose a single patch-based Convolutional Neural Network (CNN) architecture for extraction of roads and buildings from high-resolution remote sensing data. Low-level features of roads and buildings (e.g., asymmetry and compactness) of adjacent regions are integrated with Convolutional Neural Network (CNN) features during the post-processing stage to improve the performance. Experiments are conducted on two challenging datasets of high-resolution images to demonstrate the performance of the proposed network architecture and the results are compared with other patch-based network architectures. The results demonstrate the validity and superior performance of the proposed network architecture for extracting roads and buildings in urban areas.