Subpixel Mapping Technique of HSI

Spatial resolution means the minimum target that the sensor can distinguish, or the ground area expressed by a pixel point in the image. It is one of the important indexes of assessing sensor performance and remote-sensing information, and also the important basis of identifying the land object shape and size.

[1]  Hugh G. Lewis,et al.  Super-resolution target identification from remotely sensed images using a Hopfield neural network , 2001, IEEE Trans. Geosci. Remote. Sens..

[2]  Liangpei Zhang,et al.  A new sub-pixel mapping algorithm based on a BP neural network with an observation model , 2008, Neurocomputing.

[3]  J. Qi,et al.  Modification of pixel-swapping algorithm with initialization from a sub-pixel/pixel spatial attraction model , 2009 .

[4]  Robert De Wulf,et al.  Land cover mapping at sub-pixel scales using linear optimization techniques , 2002 .

[5]  Yong Ge,et al.  Development and Testing of a Subpixel Mapping Algorithm , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Hugh G. Lewis,et al.  Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network , 2003, Int. J. Geogr. Inf. Sci..

[7]  Koen C. Mertens,et al.  A sub‐pixel mapping algorithm based on sub‐pixel/pixel spatial attraction models , 2006 .

[8]  Fei Xiao,et al.  Super-resolution land-cover mapping using multiple sub-pixel shifted remotely sensed images , 2010 .

[9]  Nicholas A. S. Hamm,et al.  Fuzzy Super Resolution Mapping Based on Markov Random Fields , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[10]  L. P. C. Verbeke,et al.  Using genetic algorithms in sub-pixel mapping , 2003 .

[11]  Hugh G. Lewis,et al.  Super-resolution land cover pattern prediction using a Hopfield neural network , 2002 .

[12]  M. Q. Nguyen,et al.  Superresolution mapping using a Hopfield neural network with lidar data , 2005, IEEE Geoscience and Remote Sensing Letters.

[13]  Robert De Wulf,et al.  Sub-pixel mapping with neural networks : real-world spatial configurations learned from artificial shapes , 2003 .

[14]  Pramod K. Varshney,et al.  Super-resolution land cover mapping using a Markov random field based approach , 2005 .

[15]  P. Atkinson.  Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery , 2005 .

[16]  Alfred Stein,et al.  Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Ye Zhang,et al.  A Novel Geometry-Based Feature-Selection Technique for Hyperspectral Imagery , 2007, IEEE Geoscience and Remote Sensing Letters.