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Found 9 papers, 0 papers with code
The Segment Anything Model (SAM) for Remote Sensing Applications: From Zero to One Shot
Segmentation is an essential step for remote sensing image processing.
The Potential of Visual ChatGPT For Remote Sensing
Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation.
Counting and Locating High-Density Objects Using Convolutional Neural Network
For the tree dataset, our method returned a mean absolute error (MAE) of 2. 05, a root-mean-squared error (RMSE) of 2. 87 and a coefficient of determination (R$^2$) of 0. 986.
Semantic Segmentation with Labeling Uncertainty and Class Imbalance
Recently, methods based on Convolutional Neural Networks (CNN) achieved impressive success in semantic segmentation tasks.
A Deep Learning Approach Based on Graphs to Detect Plantation Lines
This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) for the displacement vectors between the plants.
A Review on Deep Learning in UAV Remote Sensing
In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields.
A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery
In the corn plantation datasets (with both growth phases, young and mature), our approach returned a mean absolute error (MAE) of 6. 224 plants per image patch, a mean relative error (MRE) of 0. 1038, precision and recall values of 0. 856, and 0. 905, respectively, and an F-measure equal to 0. 876.
Texture analysis using deterministic partially self-avoiding walk with thresholds
In this paper, we propose a new texture analysis method using the deterministic partially self-avoiding walk performed on maps modified with thresholds.
Texture descriptor combining fractal dimension and artificial crawlers
Since this swarm system alone does not achieve a good discrimination, we developed a new method to increase the discriminatory power of artificial crawlers, together with the fractal dimension theory.
