Spatio-Temporal Vegetation Pixel Classification By Using Convolutional Networks
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.
Code (1)
Tasks
ClassificationGeneral ClassificationSimilar Papers 제목 키워드 기반
Efficient Spatio-Temporal Vegetation Pixel Classification with Vision Transformers
Plant phenology-the study of recurrent life cycle events-is essential for understanding ecosystem dynamics and their responses to climate change impacts. While Unmanned Aerial Vehicles (UAVs) and near-surface cameras ena…
Computational EfficiencyDeep learning networks for selection of persistent scatterer pixels in multi-temporal SAR interferometric processing
In multi-temporal SAR interferometry (MT-InSAR), persistent scatterer (PS) pixels are used to estimate geophysical parameters, essentially deformation. Conventionally, PS pixels are selected on the basis of the estimated…
Computational EfficiencySemantic SegmentationAnalysis of Fractional Vegetation Coverage and its Dynamic Change in the Yalong River Basin based on Dimidiate Pixel Model
Fractional vegetation coverage (FVC) and its spatio-temporal variations are critical indicators of regional ecological changes, which are of great significance to study the laws of surface variation and analyze regional …
A CNN based method for Sub-pixel Urban Land Cover Classification using Landsat-5 TM and Resourcesat-1 LISS-IV Imagery
Time series data of urban land cover is of great utility in analyzing urban growth patterns, changes in distribution of impervious surface and vegetation and resulting impacts on urban micro climate. While Landsat data i…
Land Cover ClassificationTime SeriesTime Series AnalysisConvolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods. The vast majority of remote sensing approaches either selectively choose single cloud-free observations …
ClassificationEarth ObservationGeneral ClassificationSegmentation Of Remote Sensing Imagery