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31 August 1993 Integrated land-cover mapping from satellite imagery using artificial neural networks
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Abstract
The automatic mapping of land cover from satellite imagery requires optimal classification and spatial generalization procedures. Here we describe the use of functional ink neural networks, based on a flat perceptron net with an augmented feature vector, to generate high accuracy classification products. These can then be trained more rapidly than multi-layer perceptrons. The network output is then used to fix land cover class area statistics which control a low-level generalization procedure based on a combined iterative majority filtering and reduced class growing procedure.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Graeme G. Wilkinson, Ioannis Kanellopoulos, Z. K. Liu, and S. Folving "Integrated land-cover mapping from satellite imagery using artificial neural networks", Proc. SPIE 1941, Ground Sensing, (31 August 1993); https://doi.org/10.1117/12.154705
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