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Learning CNN Filters From User-Drawn Image Markers for Coconut-Tree Image Classification

https://doi.org/10.1109/lgrs.2020.3020098

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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F-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation

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CNN Based Technique for Automatic Tree Counting Using Very High Resolution Data

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Interactive Coconut Tree Annotation Using Feature Space Projections

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A Methodology for Neural Network Architectural Tuning Using Activation Occurrence Maps

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Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Comparing Boosted Cascades to Deep Learning Architectures for Fast and Robust Coconut Tree Detection in Aerial Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Deep Learning Based Detection of Rhinoceros Beetle Infestation in Coconut Trees Using Drone Imagery

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User-Guided Data Expansion Modeling to Train Deep Neural Networks With Little Supervision

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A Review of Individual Tree Crown Detection and Delineation From Optical Remote Sensing Images: Current progress and future

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High-resolution global map of closed-canopy coconut palm

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inferred← citeshttps://doi.org/10.5194/essd-2022-463-rc2openalex-snapshot

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User-Assisted Design of a Neural Network for Brain Tumor Segmentation

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