Adaptive pixel unmixing based on a fuzzy ARTMAP neural network with selective endmembers

Pixel unmixing is essential for the reliable description of many land-cover patterns with low spatial resolution. The fuzzy ARTMAP neural network-based model has been proven effective for pixel unmixing in the literature. In most cases, the forms of the endmember combination in diverse pixels are very distinct. However, traditional fuzzy ARTMAP model neglects such difference and models endmembers as fixed composition entities. Due to this limitation, the mixture model is unable to precisely and effectively represent details in the result image. In this work, we address this issue by applying a new selective endmember spectral mixture model based on fuzzy ARTMAP neural network. We first consider the endmember variability and identify the most suitable form of the endmember combination, and then the fuzzy ARTMAP model is used to perform the unmixing work. Through two experiments, we show that a more accurate endmember combination in the parent pixel results in an adaptive representation image. The results confirm that the proposed algorithm can effectively improve the accuracy of the unmixing results compared with the linear unmixing method and the traditional fuzzy ARTMAP model.