Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network

International Journal of Applied Earth Observation and Geoinformation

Volumn 2001, Issue 2, 2001, Pages 184-190

  Tatem, Andrew J ^a   Lewis, Hugh G ^a   Atkinson, Peter M ^a   Nixon, Mark S ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

Accuracy assessment; Constraints; Energy function; Neurons; Optimization; Remote sensing; Soft classification; Spatial resolution

Indexed keywords

ACCURACY ASSESSMENT; CLASSIFICATION; LAND COVER; OPTIMIZATION; PIXEL; REMOTE SENSING; SPATIAL RESOLUTION;

BRISTOL; ENGLAND (AVON); UNITED KINGDOM;

EID: 14244273170     PISSN: 15698432     EISSN: 1872826X     Source Type: Journal    
DOI: 10.1016/s0303-2434(01)85010-8     Document Type: Article

References (10)

1. Mapping sub-pixel proportional land cover with AVHRR imagery
2. Support vector machines for optimal classification and spectral unmixing
3. The pixel: A snare and a delusion
4. Linear spectral mixture modelling to estimate vegetation amount from optical spectral data
5. Neutral computation of decisions in optimization problems
6. Land cover simulation and prediction at the sub-pixel scale using a Hopfield neural network
7. Super-resolution target identification from remotely sensed images using a Hopfield neural network