ArticleScientific reports2024
Object-oriented multi-scale segmentation and multi-feature fusion-based method for identifying typical fruit trees in arid regions using Sentinel-1/2 satellite images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Intelligent classification of dominant tree species in urban forests based on UAV hyperspectral remote sensing images.iScience · 2026Article
- Enhanced DeepLabV3+ with OBIA and Lightweight Attention for Accurate and Efficient Tree Species Classification in UAV Images.Sensors (Basel, Switzerland) · 2025Article
- BOLM high resolution land use and land cover dataset and benchmark results for the rapidly developing City of Dhaka Bangladesh.Scientific reports · 2025Article
- Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
Fruit tree identification that is quick and precise lays the groundwork for scientifically evaluating orchard yields and dynamically monitoring planting areas. This study aims to evaluate the applicability of time series Sentinel-1/2 satellite data for fruit tree classification and to provide a new method for accurately extracting fruit tree species. Therefore, the study area selected is the Tarim Basin, the most important fruit-growing region in northwest China. The main focus is on identifying several major fruit tree species in this region. Time series Sentinel-1/2 satellite images acquired from the Google Earth Engine (GEE) platform are used for the study. A multi-scale segmentation approach is applied, and six categories of features including spectral, phenological, texture, polarization, vegetation index, and red edge index features are constructed. A total of forth-four features are extracted and optimized using the V
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