Learning multiscale and deep representations for classifying remotely sensed imagery
Published in ISPRS Journal of Photogrammetry and Remote Sensing, 2016
Recommended citation: Wenzhi Zhao, et al. (2016). "Paper Learning multiscale and deep representations for classifying remotely sensed imagery." ISPRS Journal of Photogrammetry and Remote Sensing. 113. https://www.sciencedirect.com/science/article/pii/S0924271616000137
It is widely agreed that spatial features can be combined with spectral properties for improving interpretation performances on very-high-resolution (VHR) images in urban areas. However, many existing methods for extracting spatial features can only generate low-level features and consider limited scales, leading to unpleasant classification results. [Download paper here](https://www.sciencedirect.com/science/article/pii/S0924271616000137) Recommended citation: Your Name, You. (2010). "Learning multiscale and deep representations for classifying remotely sensed imagery." ISPRS Journal of Photogrammetry and Remote Sensing. 113.