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» Deep Learning for Remote Sensing Image Understanding
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IPPS
2006
IEEE
15 years 6 months ago
Parallel morphological processing of hyperspectral image data on heterogeneous networks of computers
Recent advances in space and computer technologies are revolutionizing the way remotely sensed data is collected, managed and interpreted. The development of efficient techniques ...
Antonio J. Plaza
ICDM
2009
IEEE
207views Data Mining» more  ICDM 2009»
14 years 10 months ago
Spatially Adaptive Classification and Active Learning of Multispectral Data with Gaussian Processes
Multispectral remote sensing images are widely used for automated land use and land cover classification tasks. Remotely sensed images usually cover large geographical areas, and s...
Goo Jun, Ranga Raju Vatsavai, Joydeep Ghosh
ECCV
2002
Springer
16 years 2 months ago
The Relevance of Non-generic Events in Scale Space Models
In order to investigate the deep structure of Gaussian scale space images, one needs to understand the behaviour of spatial critical points under the influence of blurring. We sho...
Arjan Kuijper, Luc Florack
JGS
2002
78views more  JGS 2002»
14 years 11 months ago
Emerging and vector-borne diseases: Role of high spatial resolution and hyperspectral images in analyses and forecasts
Many infectious diseases that are emerging or transmitted by arthropod vectors have a strong link to landscape features. Depending on the source of infection or ecology of the tran...
Mark L. Wilson
IGARSS
2009
14 years 9 months ago
Active Learning of Hyperspectral Data with Spatially Dependent Label Acquisition Costs
Supervised learners can be used to automatically classify many types of spatially distributed data. For example, land cover classification by hyperspectral image data analysis is ...
Alexander Liu, Goo Jun, Joydeep Ghosh