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The effective recognition and precise positioning of multiscale building rooftop is one of the key scientific problems that have yet to be resolved urgently in the current implementation of high-resolution remote sensing. In recent years, the automatic recognition of high-resolution image targets often employs convolutional neural networks to extract features. However, such traditional methods often ignore multiscale features of geographical objects, while lacking effective multiscale information extraction strategies. By utilizing the feature learning capability of deep neural networks, this study proposes a multiscale convolutional neural network named MS-CNN to recognize building rooftops from high-resolution remote sensing imagery. In addition, this study constructs a pedigree deep learning sample library based on the remote sensing Tupu theory that considers the spectral and geometric characteristics of building rooftops. Able to utilize feature segmentation mechanism and fusion enhancement strategy, MS-CNN enriches the receptive fields obtained by each convolution layer. The proposed network of this study is also compared with the famous Mask R-CNN method, proving the relative advantages of the MS-CNN method with multiscale characteristics. The experimental results show that the precision and recall metrics of the MS-CNN are 4.18% (.8655 vs. .8238) and 5.71% (.8380 vs. .7809) higher than those of the Mask R-CNN, respectively. The proposed method has been deployed in practical engineering projects in Vietnam and Myanmar, etc.

MS-CNN: multiscale recognition of building rooftops from high spatial resolution remote sensing imagery