Assessment of a fully soft classification approach using back propagation neural network in estimating of rice growing area

La Chen

doi:10.1117/12.887191

4 February 2011 Assessment of a fully soft classification approach using back propagation neural network in estimating of rice growing area

La Chen

Author Affiliations +

Proceedings Volume 7752, PIAGENG 2010: Photonics and Imaging for Agricultural Engineering; 775215 (2011) https://doi.org/10.1117/12.887191
Event: International Conference on Photonics and Image in Agricultural Engineering (PIAGENG 2010), 2010, Qingdao, China

Abstract

Mixed pixels are a major problem as a conventional classification will force the allocation of a mixed pixel to one class, which need not even be one of the component classes of that pixel. Since the conventional classification output is "hard", comprising of only the code of the allocated class, such techniques cannot therefore be used appropriately to represent mixed pixels. The fully soft classifications were used to accommodate mixed pixel problem at each stage of classification. More than 90% of rice is planted in southern China where population density is very high and rice planting is often conducted by unit of single firmly, thus the size of paddy field patches are very small and the shape of those are not often irregular. For estimating rice-growing field area using remotely sensing data, the mixed pixel problems are more severe. In this study, an approach to achieve such a fully soft classification using back propagation neural network (BPN) in the rice growing region was assessed. The remote sensing data used in this study is a simulated imagery from TM data and a rice field map investigated by GPS. It was found this approach can improve significantly classification accuracy for rice-growing field harden mapping and total area estimating at sub-pixel level.

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La Chen "Assessment of a fully soft classification approach using back propagation neural network in estimating of rice growing area", Proc. SPIE 7752, PIAGENG 2010: Photonics and Imaging for Agricultural Engineering, 775215 (4 February 2011); https://doi.org/10.1117/12.887191

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KEYWORDS

Image classification

Remote sensing

Neural networks

Fuzzy logic

Roads

Global Positioning System

Algorithm development

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