Selecting a subset of spectral bands for mapping invasive alien plants: a case of discriminating Parthenium hysterophorus using field spectroscopy data

Abstract

ABSTRACTParthenium hysterophorus is considered one of the top seven most problematic and devastating weeds in the world. It compromises the integrity of ecosystems, human health, agricultural production, and biodiversity. Therefore, its early detection and discrimination are critical for facilitating site-specific weed management. Recently, adoption of remote-sensing approaches has gained popularity for species-level mapping of vegetation. Specifically, the use of hyperspectral data has demonstrated reliable mapping accuracy. However, when working with hyperspectral data, feature selection is fundamental to achieving reliable classification accuracies. Moreover, challenges such as ‘the curse of dimensionality’ that cause unstable parameter estimates and high generalization errors when the number of observations (n) is less than the number of descriptive variables (p), i.e. n < p often compromise classification accuracy. In this study, we assessed the potential of a hybrid feature selection approach, based on statistical analysis and Support Vector Machines – Recursive Feature Elimination (SVM-RFE) for determining a subset of hyperspectral bands relevant for discriminating P. hysterophorus using field spectroscopy data. We compared the performance of SVM-RFE, Random Forest variable importance (RF VarImp), and entire spectral dat aset (p = 1633) using SVM classifier with radial basis function (RBF) kernel. Results of SVM-RFE and RF VarImp generated lower classification accuracies (i.e. 76.19% and 66.67%, respectively) than the entire spectral data set, i.e. 78.57%. On the other hand, using a subset of 10 spectral bands, our hybrid approach yielded a superior overall accuracy of 80.19% in discriminating P. hysterophorus from its co-occurring species. The study showed that a subset consisting of two red-edge bands located at 685 and 707 nm, one near infrared band at 1115 nm, and seven short wave infrared bands at 1971, 1982, 1990, 1966, 2003, 2005, and 2013 nm had the greatest potential for discrimination of P. hysterophorus and co-occurring plant groups. Overall, the study suggests that the hybrid approach is effective for early detection and improvement of invasive alien plants classification accuracy, reducing data dimensionality and selecting a relevant spectral subset of bands.