Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data

Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data

Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop show...

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Bibliographic Details
Main Authors: Jiao Guo, Henghui Li, Jifeng Ning, Wenting Han, Weitao Zhang, Zheng-Shu Zhou
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Remote Sensing
Subjects:
crop classification
polarimetric synthetic aperture radar (polsar)
multi-temporal
stacked sparse auto-encoder (s-sae)
convolutional neural network (cnn)
Online Access:https://www.mdpi.com/2072-4292/12/2/321

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https://www.mdpi.com/2072-4292/12/2/321

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