High Quality Prime Farmland (HQPF) is high, stable yields based on land consolidation of prime farmland, and has its
important impact upon China's food security. To make clear the status-in-quo of the HQPF is important to its
construction and management. However, it is difficult to get the spatial distribution information of the constructed HQPF
enough rapidly in mountainous area using ground investigation, as well as hard to satisfy the requirements of large-scale
promotion. A HQPF extraction framework based on object-oriented image analysis is discussed and applied to aerial
imageries of Tonglu County. The approach can be divided into 3 steps: image segmentation, feature analysis & feature
selection and extraction rules generation. In the image segmentation procedure, canny operator is used in edge detection,
an edge growth algorithm is used to link discontinuous edge, and region labelling is carried out to generate image object.
In the feature analysis & selection procedure, object-oriented feature analysis and feature selection methods are also
discussed to construct a feature subset with fine divisibility for HQPF extraction. In the extraction rules generation
procedure, the C4.5 algorithm is used to establish and trim the decision tree, then HQPF decision rules are generated
from the decision tree. Compared with supervised classification (MLC classifier, ERDAS 8.7) and another object-oriented
image analysis method (FNEA, e-Cognition4.0), the accuracy assessment shows that the extraction results by
the object-oriented extraction patters have a high level of category consistency, size consistency and shape consistency.