Object-based convolutional neural network for high-resolution imagery classification
Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017
Recommended citation: Wenzhi Zhao, et al. (2017). "Object-based convolutional neural network for high-resolution imagery classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(7). https://ieeexplore.ieee.org/abstract/document/7890382/
Timely and accurate classification and interpretation of high-resolution images are very important for urban planning and disaster rescue. However, as spatial resolution gets finer, it is increasingly difficultto recognize complex patterns in high-resolution remote sensing images. Deep learning offers an efficient strategy to fill the gap between complex image patterns and their semantic labels. However, due to the hierarchical abstract nature of deep learning methods, it is difficult to capture the precise outline of different objects at the pixel level.
Recommended citation: Wenzhi Zhao, et al. (2017). “Object-based convolutional neural network for high-resolution imagery classification” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 10(7).