Segmentation of multispectral remote sensing images using active support vector machines

@article{Mitra2004SegmentationOM,
  title={Segmentation of multispectral remote sensing images using active support vector machines},
  author={Pabitra Mitra and B. Uma Shankar and Sankar K. Pal},
  journal={Pattern Recognition Letters},
  year={2004},
  volume={25},
  pages={1067-1074}
}
The problem of scarcity of labeled pixels, required for segmentation of remotely sensed satellite images in supervised pixel classification framework, is addressed in this article. A support vector machine (SVM) is considered for classifying the pixels into different landcover types. It is initially designed using a small set of labeled points, and subsequently refined by actively querying for the labels of pixels from a pool of unlabeled data. The label of the most interesting/ ambiguous… CONTINUE READING
Highly Cited
This paper has 174 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 121 extracted citations

174 Citations

0102030'07'10'13'16
Citations per Year
Semantic Scholar estimates that this publication has 174 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 20 references

1067–1074 on Machine Learning

  • P. Mitra
  • Pattern Recognition Letters
  • 2004
Highly Influential
4 Excerpts

An assessment of support vector machines for land cover classification

  • C. Huang, L. S. Davis, J.R.G. Townshend
  • International Journal of Remote Sensing
  • 2002
1 Excerpt

Similar Papers

Loading similar papers…