A hybrid method combining SOM-based clustering and object-based analysis for identifying land in good agricultural condition
Remotely sensed imagery is currently used as an efficient tool for agricultural management and monitoring. In addition, the use of remotely sensed imagery in Europe has been extended towards determination of the areas potentially eligible for the farmer subsidies under the Common Agricultural Policy (CAP), through interactive or automatic land cover identification. For accurate quantification and fast identification of agricultural land cover areas from the imagery, a hybrid method, which combines automated clustering of self-organizing maps with object based image analysis, and called SOM+OBIA, is proposed. Performance analysis on three test zones (using multi-temporal Rapideye imagery) indicates that for the basic land cover categories (forest, water, vegetated areas, bare areas and sealed surfaces), unsupervised classification with the proposed SOM+OBIA method achieves an identification accuracy comparable to the accuracy of the traditional interactive object oriented analysis, with considerably less user interaction.
good agricultural condition land cover identification self-organizing maps object based image analysis unsupervised clustering conn linkage