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.

K. Taşdemir, P. Milenov and B. Tapsall, “A hybrid method combining SOM-based clustering and object-based analysis for identifying land in good agricultural condition,” Computers and Electronics in Agriculture, 83, 92-101, 2012.

Vector quantization based approximate spectral clustering

Spectral partitioning, recently popular for unsupervised clustering, is infeasible for large datasets due to its computational complexity and memory requirement.Therefore,approximate spectral clustering of data representatives (selected by various sampling methods) was used. Alternatively,we propose to use neural networks (self-organizing maps and neural gas),which are shown successful in quantization with small distortion, as preliminary sampling for approximate spectral clustering (ASC).We show that they usually outperform k-means sampling (which was shown superior to various sampling methods), in terms of clustering accuracy obtained by ASC. More importantly, for quantization based ASC, we introduce a local density-based similarity measure – constructed without any user-set parameter – which achieves accuracies superior to the accuracies of commonly used distance based similarity.

K. Taşdemir, “Vector quantization based approximate spectral clustering of large datasets,” Pattern Recognition, 45 (8), 3034-3044 , 2012.