Study On Automatic Cultivated Land Extraction From High Resolution Satellite Imagery Based On Knowledge

Cultivated land protection has always been the core and focus of land resource management, the cultivated land quantity and distribution of information acquisition is the precondition of achieving this goal. Remote sensing technology is applied to get cultivated land information objectively from a wide-area and local-area spatial scale. Very high resolution (VHR) satellite imagery is used widely for large scale land use survey and monitoring. Due to its highly detailed information, large data volume, big differences within the same classes, VHR satellite imagery is difficult to be interpreted automatically and is always managed in the means of manual visual interpretation in the actual operation, lacking a high degree of automation of process-oriented way of working. Object-oriented image analysis (OBIA) technology is suitable for VHR satellite imagery information extraction. However, OBIA technology has not been used in the actual business, which is caused by the constraints in the process of segmentation and classification, for example, the selection of segmentation algorithm, the classification features selection, and the determination of theresholds. Taking cultivated land extraction on the outskirts of cities of VHR satallited imagery as the main line and combining with the knowledge-based classification fundamental, such as knowledge acquision, knowledge representation, knowledge reasoning, and knowledge accumulation, this paper achieves a rapid and real-time extraction of cultivated land in the form of classification rule set on the basis of enhancing OBIA automation level. The main research and work involves the following aspects:(1) An universal knowledge framework for VHR imagery information extraction is established. From the perspective of information extraction, a detailed knowledge framework is built to supply the classification rule set for cultivated land, construction land, and other land cover types with feature library from four aspects of interpretation imagery, interpretation area, interpretation objects and geographical secondary data, which is combined with the basic process of visual interpretation and the use of geographical knowledge.(2) Hierarchical extraction strategy for cultivated land is accessed automatically on the basis of the improvement of the SEaTH (Seperability and Thersholds) algorithm. The SEaTH algorithm is improved to solve the problems of cultivated land extraction, such as the low degree of automation and poor universality. Firstly, automatic feature selection and thresholds determination is reliazed; and then cultivated land extraction rule set is constructed hierarchically and automatically by analyzing the similarity of distinction process and the degree of distinguishment between cultivated and non-cultivated land, in terms of several principles about information extraction, for example, from easy to difficult, from less to more, priority in the use of non-texture features.(3) A promotion and adjustment scheme is presented for cultivated land extraction rule set under the condition of multi-temporal images in the same area. Considering possible changes of classification features, rule thresholds, and extraction orders when applying the cultivated land extraction rule set from the source image to the target image, a promotion and adjustment scheme is presented to achieve an effective repeated use of rule set on the premise of a high classification accuracy.(4) The rules engine technology is introduced into the remote sensing image information extraction process to achieve the rapid extraction of the cultivated land based on the expert system. An open source rule engine tool NxBRE and its knowledge representation specification RuleML are utilized to estabilish the knowledge base files. The custom and management of rules, interface display, and knowledge resoning function modules are developed to isolate the applyment of cultivated land extraction and the implementation of code, and to ealize the expression, storage, update, execution, and visualization of rule set, promoting the practical application of relevant research results.