Name
Title | ||
|---|---|---|
Hierarchical Segmentation Of Remote Sensing Images By Unsupervised Deep Learning Features |
Abstract | ||
|---|---|---|
Due to the scale diversity of geographical objects, hierarchical remote sensing image segmentation plays an important role in object-oriented image analysis. In this paper, a hierarchical remote sensing images segmentation method with unsupervised deep learning features is proposed. The unsupervised deep learning features of an image are extracted with a sparse convolutional auto-encoder. Both deep features and the clustering information of deep features are utilized in hierarchical image segmentation which is a bottom-up region merging process. The iterative region merging process starts from an initial partition of an image under a merging criterion. Finally, a tree-like image segmentations hierarchy which contains ground objects of different scales is obtained. The proposed method integrates the advantages of unsupervised deep learning features with region merging-based hierarchical image segmentation. The experiment results have shown the proposed method is superior to the methods using only spectral or spatial features. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISCID.2017.79 | 2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL. 1 |
Keywords | Field | DocType |
hierarchical image segmentation, sparse convolutional auto-encoder, region adjacency graph, nearest neighbor graph, remote sensing images | Iterative reconstruction,Pattern recognition,Segmentation,Computer science,Remote sensing,Image segmentation,Feature extraction,Artificial intelligence,Deep learning,Hierarchy,Merge (version control),Cluster analysis | Conference |
ISSN | Citations | PageRank |
2165-1701 | 0 | 0.34 |
References | Authors | |
2 | 3 | |