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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2020

08.06.2020

A Novel Technique for Segmentation of High Resolution Remote Sensing Images Based on Neural Networks

Erschienen in: Neural Processing Letters | Ausgabe 1/2020

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Abstract

Remote sensing images have become one of the most important imaging resources recently. Thus, it is important to develop high-performance techniques to process and manipulate these images. On the other hand, image processing techniques are enhanced spatially based on neural networks. Deep learning is one of the most important techniques in use for computer vision tasks and has been deployed successfully to solve many tasks. But when dealing with remote sensing images, the deep learning method faces two main problems: the underfitting problem, because of the small amount of learning data and the unbalanced receptive field problem, because of the structural stereotype of the remote sensing images. In this paper, we propose to use a complex-valued neural network to segment high-resolution remote sensing images. The proposed network can deal with the problems of remote sensing images by using an ensemble of Complex-Valued Auto-Encoder. Based on an adaptive clustering technique, this network can be used to solve the multi-label segmentation problem of remote sensing images. The proposed method achieves state-of-the-art performance when evaluated on the ISPRS 2D dataset.
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Metadaten
Titel
A Novel Technique for Segmentation of High Resolution Remote Sensing Images Based on Neural Networks
Publikationsdatum
08.06.2020
Erschienen in
Neural Processing Letters / Ausgabe 1/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-020-10280-1

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