Improving the Reliability of Mixture Tuned Matched Filtering Remote Sensing Classification Results Using Supervised Learning Algorithms and Cross-Validation

Abstract

Mixture tuned matched filtering (MTMF) image classification capitalizes on the increasing spectral and spatial resolutions of available hyperspectral image data to identify the presence, and potentially the abundance, of a given cover type or endmember. Previous studies using MTMF have relied on extensive user input to obtain a reliable classification. In this study, we expand the traditional MTMF classification by using a selection of supervised learning algorithms with rigorous cross-validation. Our approach removes the need for subjective user input to finalize the classification, ultimately enhancing replicability and reliability of the results. We illustrate this approach with an MTMF classification case study focused on leafy spurge (Euphorbia esula), an invasive forb in Western North America, using free 30-m hyperspectral data from the National Aeronautics and Space Administration’s (NASA) Hyperion sensor. Our protocol shows for our data, a potential overall accuracy inflation between 18.4% and 30.8% without cross-validation and according to the supervised learning algorithm used. We propose this new protocol as a final step for the MTMF classification algorithm and suggest future researchers report a greater suite of accuracy statistics to affirm their classifications’ underlying efficacies.

Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods

“…Finally, BR, PCA and Mixture Tuned MF methods were adopted (Pour & Hashim, 2015;Pour et al, 2018;Routh et al, 2018) to obtain hydrothermal alteration and iron oxide from Landsat 8 satellite data.…”

Utilization of Sentinel-2 Imagery in the Estimation of Plastics Among Floating Debris Along the Coast of Manila Bay

“…5 is an example that contains data values that have high MF score and low infeasibility. However, according to Routh et al (2018), this method could be subjective, as the user would be the one to select the appropriate values for the MF score and Infeasibility in a scatterplot. As an alternative, the summary statistics of the plastics used as training pixels were analyzed.…”

Análise do Modelo Linear de Mistura Espectral na Avaliação de Incêndios Florestais no Parque Nacional do Araguaia, Tocantins, Brasil: Imagens EO-1/Hyperion e Landsat-7/ETM+

“…As análises de avaliação da (FQ) nos pixels foi realizada a partir da binarização dos dados com base na (presença / ausência) de (AQ) de acordo com o modelo de mistura e classificação de imagens propostos por (Routh et al, 2018). Dessa forma, a estrutura de dados viabilizou a obtenção de resultados significativos a partir de uma classificação em vários níveis de proporção de pixels ausentes de mistura espectral de áreas queimadas por incêndio.…”