[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.7848/ksgpc.2013.31.6-2.559

Increasing Spatial Resolution of Remotely Sensed Image using HNN Super-resolution Mapping Combined with a Forward Model

Minh, Nguyen Quang (Faculty of Surveying and Mapping, Hanoi University of Mining and Geology)
Huong, Nguyen Thi Thu (Faculty of Surveying and Mapping, Hanoi University of Mining and Geology)

Publication Information

Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.31, no.6_2, 2013 , pp. 559-565 More about this Journal

Abstract

Spatial resolution of land covers from remotely sensed images can be increased using super-resolution mapping techniques for soft-classified land cover proportions. A further development of super-resolution mapping technique is downscaling the original remotely sensed image using super-resolution mapping techniques with a forward model. In this paper, the model for increasing spatial resolution of remote sensing multispectral image is tested with real SPOT 5 imagery at 10m spatial resolution for an area in Bac Giang Province, Vietnam in order to evaluate the feasibility of application of this model to the real imagery. The soft-classified land cover proportions obtained using a fuzzy c-means classification are then used as input data for a Hopfield neural network (HNN) to predict the multispectral images at sub-pixel spatial resolution. The 10m SPOT multispectral image was improved to 5m, 3,3m and 2.5m and compared with SPOT Panchromatic image at 2.5m resolution for assessment.Visually, the resulted image is compared with a SPOT 5 panchromatic image acquired at the same time with the multispectral data. The predicted image is apparently sharper than the original coarse spatial resolution image.

Keywords

Hopfield neural network optimization; Soft classification; Image downscaling; Forward model;

Citations & Related Records

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