Training method for multi-output land cover classification model, classification method, and device
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United States of America
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Nov 26, 2024
Grant Date -
Jan 6, 2022
app pub date -
Nov 25, 2020
filing date -
Nov 25, 2020
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Abstract
A training method for multi-output land cover classification model and a classification method are provided. The training method includes: obtaining a training data; inputting the training data into an initial model based on deep belief nets for training to obtain a multi-output land cover classification model, wherein the initial model includes N level outputs, and the N level outputs include an output set at last network layer and (N−1) level output set at any (N−1) network layers from a first network layer to a penultimate network layer of the initial model; determining a total loss according to losses of the N level outputs; performing a backpropagation based on the total loss to adjust a parameter of the initial model, N being an integer greater than or equal to 2. The gradient is not easy to disappear during backpropagation of the model, which is beneficial to improve classification accuracy.
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