Exploiting spectral and spatial information for the identification of hazelnut fields using self-organizing maps

Automatic land-cover identification using remote-sensing images is essential for agricultural management and monitoring, which is an ongoing challenge. For permanent crops, which are of great importance economically and environmentally, it becomes even more challenging mainly due to the varying statistics of orchards such as the existence of different orchard types, different crown sizes even for the same type, different distances between orchards among various fields and overlapping crowns. This challenge necessitates the utilization of both spectral values and spatial relations of pixels. To accurately determine the fields of permanent crops, hazelnuts in particular, a classification system with hybrid learning, which merges an image features map (IFM) and learning vector quantization (LVQ), is proposed in this study. IFM is a variant of a self-organizing map (an unsupervised neural learning paradigm successfully used in many applications of remote-sensing imagery), which exploits both spectral and spatial information without additional computation of texture. LVQ, however, is supervised learning for fine-tuning of class boundaries. Experimental results on finding hazelnut fields using multispectral QuickBird imagery indicate that the proposed method achieves acceptable accuracies and often produces more accurate extraction than the accuracies obtained based only on spectral or on spatial information.