Considering the economic and environmental aspects, hazelnut orchards are of great importance in Turkey. It is crucial to develop methods for detecting, monitoring, protecting and managing these orchards. This can be done exactly and fast by evaluating the remote sensing images of these areas. For this aim clustering methods are so popular due to their unsupervised nature. Particularly, spectral clustering can be useful thanks to its ability to extract clusters of different structures and its easy implementation. However, its direct use in remote sensing images is infeasible due to its high computational and memory cost. That's why its indirect implementation, which is named approximate spectral clustering (ASC), is applied on quantized or sampled prototypes of the image. ASC not only takes advantages of spectral clustering but also utilizes different information types (such as topology and density) on the prototype level for effective similarity definition. Although ASC can be used with various similarity measures, the optimum measure depends on the approximation method (sampling/quantization), dataset and application. Hence, in this study, by introducing Selective Sampling based Approximate Spectral Clustering Ensemble method (SSASCE), we eliminate the need to determine the optimum measure and harness their individual advantages. We then show the outperformance of SSASCE on extraction of hazelnut fields.