# Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks

## TL;DR

This paper introduces a convolutional network-based method for classifying vegetation pixels in high-resolution images over time, aiding plant phenology studies by handling complex spatial-temporal data.

## Contribution

The paper presents a novel ConvNet approach specifically designed for spatio-temporal vegetation pixel classification in high-resolution imagery, addressing challenges like data heterogeneity and missing data.

## Key findings

- Effective in classifying vegetation pixels in high-res images
- Outperforms existing spatio-temporal classification strategies
- Validated on Brazilian Cerrado biome datasets

## Abstract

Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on Convolutional Networks (ConvNets) to perform spatio-temporal vegetation pixel-classification on high resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies.