Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
This addresses the challenge of plant phenology monitoring for researchers, but it is incremental as it applies an existing method (ConvNets) to a specific domain with new data.
The paper tackles the problem of locating and identifying plant species over time and space using high-resolution UAV images, proposing a convolutional network method that effectively overcomes other spatio-temporal pixel-classification strategies in experiments on Brazilian Cerrado datasets.
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.