Abstract
This article addresses the pixelwise classification problem based on temporal profiles, which are encoded in 2-D representations based on recurrence plots, Gramian angular/ difference fields, and Markov transition field. We propose a multirepresentational fusion scheme that exploits the complementary view provided by those time series representations and different data-driven feature extractors and classifiers. We validate our ensemble scheme in the problem related to the classification of eucalyptus plantations in remote sensing images. Achieved results demonstrate that our proposal overcomes recently proposed baselines, and now represents the new state-of-the-art classification solution for the target dataset.
Highlights
P IXELWISE remote sensing image classification has been established as an active research area
A promising research venue relies on the development of classification systems based on time series associated with pixels [e.g., time series associated with vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced
This article addressed the pixelwise remote sensing image classification problem based on patterns found in time series associated with pixels
Summary
P IXELWISE remote sensing image classification has been established as an active research area. A promising research venue relies on the development of classification systems based on time series associated with pixels [e.g., time series associated with vegetation indices, such as normalized difference vegetation index (NDVI) or enhanced. The values of the vegetation indices of the pixels are grouped together as time series and undergo transformations in such a way that the 1-D series is represented as a 2-D matrix. We can use this matrix as an input image for feature extractors. This is the f (Ti, Tj )DIF = |Ti − Tj | (1) f (Ti, Tj )DIV = Ti Tj (2). The dark green time series is associated with a pixel from an eucalyptus region, while the orange time
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