This paper presents a supervised hierarchical remote sensing image segmentation technique using a committee of multi-scale convolutional neural networks. With existing techniques, segmentation is achieved through fine-tuning a set of predefined feature detectors. However, such a solution is not robust since the introduction of new sensors or applications would require novel features and techniques to be developed. Conversely, the proposed method achieves segmentation through a set of learned feature detectors. In order to learn feature detectors, the proposed method exploits a committee of convolutional neural networks that perform multi-scale analysis on each band in order to derive individual probability maps on region boundaries. Probability maps are then inter-fused in order to produce a fused probability map. Further, the fused map is intra-fused using a morphological scheme into a hierarchical segmentation map. The proposed method is quantitatively compared to baseline techniques on a publiclyavailable dataset. Results, presented in this paper, highlight the improved accuracy of the proposed method.
Basaeed et al. REF proposed a supervised hierarchical segmentation for remote sensing images.
A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks
Journal of remote sensing
Computer Science