A new direction in Earth observations from space: IKONOS

Zanoni, Vicki; Goward, Samuel N.

doi:10.1016/j.rse.2003.08.011

Cited by 7 publications

(3 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Started from a single cluster, the K-mean clustering algorithm with undetermined number of class [10] continuously divided the clusters until the cluster validity index reached its minimum value. The cluster validity index [11] is defined as (1) where c is the total number of classes, i is the mean center of class i, n i is the number of pixels in class i, x ik is the kthpixel of class i, and n is the total number of pixels.…”

Section: The Distributed K-mean Clustering Algorithmmentioning

confidence: 99%

“…With the revolutionizing advents in sensor technology, high spatial and/ or spectral resolution remote sensing images are increasingly produced. For example, high-resolution images present a new challenge over, in that a huge amount of data must be analyzed for investigation of wide area of interest [1][2][3]. Considering the amount of data in need of processing and the high computational costs required by image processing algorithms, conventional computing environments are simply impractical.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Tree identification using a distributed K-mean clustering algorithm

Fan

Tzeng

Lin

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

Trees play an important role in maintaining environmental conditions suitable for life on the earth. To classify the tree type is very important for the forest maintenance. With the advent of high spatial resolution remote sensing sensors, our ability has greatly increased for tree type identification. Considering the amount of data in need of processing and the high computational costs required by image processing algorithms, conventional computing environments are simply impractical. Therefore, it is necessary to develop techniques and models for efficiently processing large volume of remote sensing images. In this study, a cluster computing environment was adopted to speed up the computation time. The test image was first partitioned into hundreds of manageable sub-images. Scheduled by the head node, the sub-images were then distributed to compute nodes for processing. A distributed K-mean clustering algorithm with undetermined number of class was applied to each compute node. A promising result was obtained. Compared to the field investigations, tree types of the test site were properly identified. In addition, great improvement in computation time was obtained. The distributed K-mean clustering algorithm implemented on our cluster computing environment performed much faster than stand-alone alternatives. By adding more compute nodes to our cluster computing environment, further improvement in computation time is expected.

show abstract

Section: The Distributed K-mean Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tree identification using a distributed K-mean clustering algorithm

Fan

Tzeng

Lin

et al. 2010

2010 IEEE International Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

show abstract

“…In 1999, IKONOS was launched and provided 1 m panchromatic images of the Earth surface (e.g. Zanoni and Goward, 2003). With the launch of Quick-Bird2 in 2001 (60 cm resolution) and of SPOT5 in 2002 (2.5 m resolution), three very high-spatialresolution satellite datasets were available for the analysis of volcanic terrains and eruptive phenomena.…”

Section: Introductionmentioning

confidence: 99%