A supervised hierarchical segmentation of remote-sensing images using a committee of multi-scale convolutional neural networks

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 learnt 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 confidence maps on region boundaries. Confidence maps are then inter-fused in order to produce a fused confidence map. Furthermore, 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 publicly available data set. The results presented in this paper highlight the improved accuracy of the proposed method

Beyond pan-sharpening: Pixel-level fusion in remote sensing applications

Abstract—The field of remote sensing is associated with an increasing amount of imagery data, mission after mission. Such an increase makes the application of image fusion techniques in remote sensing important. However, the interrelationship between the two fields is not well-understood. In fact, the term “image fusion ” in remote sensing has usually been limited to one application only, that is pan-sharpening. Image fusion, however, is much broader and can be applied to serve different purposes within the field of remote sensing. This paper aims to show where pan-sharpening fits within the image fusion paradigm, to present some other applications of image fusion in remote sensing, and to highlight the advantages that image fusion can provide. Index Terms—Pixel-level image fusion, image fusion, data fusion, pan-sharpening, remote sensing, image processing, signal processing. I