High Quality Prime Farmland extraction pattern based on object-oriented image analysis

Abstract

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.