Sep 20, 2019 · The DML model is supervised by the center loss to produce spectrum-based features that gather more tightly in Euclidean space within classes.
A novel framework that organically combines the spectrum-based deep metric learning (DML) model and the conditional random field (CRF) algorithm is proposed ...
Jul 16, 2019 · The deep metric learning model is supervised by the center loss to produce spectrum-based features that gather more tightly in Euclidean space ...
The deep metric learning model is supervised by center loss, and is used to produce spectrum-based features that gather more tightly within classes in Euclidean ...
The deep metric learning model is supervised by center loss, and is used to produce spectrum-based features that gather more tightly within classes in Euclidean ...
Jul 15, 2019 · To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two ...
Recurrent 3D-CNN. 99.50%. Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field. 2019. 21. A-SPN. 99.24%. Attention-Based ...
Oct 22, 2024 · The semantic segmentation of hyperspectral images through spectral and spatial information via a framework consisting of CNN and CRF is ...
In this survey, we delve into a comprehensive examination of diverse deep learning models for hyperspectral image classification.
Missing: Conditional | Show results with:Conditional
This paper proposes a method to classify hyperspectral images by considering both spectral and spatial information via a combined framework consisting of ...