Skip to Main content Skip to Navigation
Conference papers

Extending K-means to Preserve Spatial Connectivity

Abstract : Clustering is one of the most important steps in the data processing pipeline. Of all the clustering techniques, perhaps the most widely used technique is K-Means. However, K-Means does not necessarily result in clusters which are spatially connected and hence the technique remains unusable for several remote sensing, geoscience and geographic information science (GISci) data. In this article, we propose an extension of K-Means algorithm which results in spatially connected clusters. We empirically verify that this indeed is true and use the proposed algorithm to obtain most significant group of wa-terbodies mapped from multispectral image acquired by IRS LISS-III satellite.
Document type :
Conference papers
Complete list of metadatas

Cited literature [9 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01686321
Contributor : Sampriti Soor <>
Submitted on : Wednesday, January 17, 2018 - 11:31:35 AM
Last modification on : Wednesday, February 26, 2020 - 7:06:07 PM
Document(s) archivé(s) le : Monday, May 7, 2018 - 1:21:01 PM

File

KMeansGraph_IGARSS2018V4.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01686321, version 1

Citation

Sampriti Soor, Aditya Challa, Sravan Danda, B Daya Sagar, Laurent Najman. Extending K-means to Preserve Spatial Connectivity. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2018, Valencia, Spain. ⟨hal-01686321⟩

Share

Metrics

Record views

514

Files downloads

625