NDLI: Hierarchical classification of land-cover types using RAG-based merging

Content Provider	IEEE Xplore Digital Library
Author	Sang-Hoon Lee
Copyright Year	2007
Description	Author affiliation: Kyungwon Univ. Seongnam, Seongnam (Sang-Hoon Lee)
Abstract	A multistage hierarchical clustering technique, which is an unsupervised technique, was suggested in this paper for classifying large remotely-sensed imagery. The multistage algorithm consists of two stages. The "local" segmentor of the first stage performs region-growing segmentation by employing a RAG-based merging with the restriction that pixels in a cluster must be spatially contiguous. The "global" segmentor of the second stage, which has not spatial constraints for merging, clusters the segments resulting from the previous stage. The second stage is an agglomerative hierarchical clustering procedure which merges the best MCN defined in spectral space, and then generates a dendrogram which represents a hierarchy of consecutive merging processes. The experimental results show that the new approach proposed in this study is quite efficient to analyze very large images. The technique was then applied to classify the land-cover types using the remotely-sensed data acquired from the Korean peninsula.
Starting Page	2060
Ending Page	2062
File Size	1538156
Page Count	3
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Merging Image segmentation Clustering algorithms Image classification Remote sensing Image analysis Partitioning algorithms Industrial engineering Parameter estimation Unsupervised learning dendrogram segmentation classification unsupervised analysis region growing multiwindow operation hierarchical clustering RAG
Content Type	Text
Resource Type	Article

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