Supervised remote sensing image segmentation using boosted convolutional neural networks
In this paper, a region segmentation technique for remote sensing images using a boosted committee of Convolutional Neural Networks (CNNs) coupled with inter band and intra band fusion, is proposed. The vast heterogeneity in remote sensing images restricts the application of existing segmentation methods that often rely on a set of predefined feature detectors along with tunable parameters. Therefore, it is highly challenging to design a segmentation technique which could achieve high accuracy while simultaneously maintaining strong generalization particularly for visual data with improved spatial, spectral, and temporal resolutions. The proposed method is a fusion framework consisting of a set of thirty boosted networks that derive individual probability maps on the location of region boundaries from the different multi spectral bands and combines them into one using an averaging inter band fusion scheme. The boundaries are then thinned, connected, and region segmented using a morphological intra band fusion scheme. Qualitative and quantitative results, on publicly available datasets, confirm the superiority of the proposed segmentation method over existing state of art techniques. In addition, the paper also demonstrates the effect of some variations in design choices of the proposed method.
Knowl. Based Syst.