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Erschienen in:
Multi-view Contrastive Clustering with Clustering Guidance and Adaptive Auto-encoders Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

MADB-RemdNet for Few-Shot Learning in Remote Sensing Classification

verfasst von : Kun Wang, Yingying Wang, Zhiming Ding

Erschienen in: Spatial Data and Intelligence

Verlag: Springer Nature Singapore

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Abstract

The problem of small sample classification is to identify image categories that have not appeared in the training concentration when marking the scarce sample samples of the training data set. Such tasks are of great significance in the recognition of remote sensing scenarios. It is a problem worth studying in this field. As we all know, training a deep learning model for classification requires a considerable labeling data set, which makes the production of training data sets huge. In this article, we propose a MADB feature extraction model based on Mixed Attention Module as a base model to extract features. Using RccaEMD module as the measurement algorithm to distinguish the classification of remote sensing scenarios. In NWPU-RESISC45 dataset, AID dataset, and UC-Merced dataset, it proves that our method has achieved higher accuracy than the current advanced methods of this field.

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Vorheriges Kapitel Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model
Nächstes Kapitel Convolutional Neural Network Based on Multiple Attention Mechanisms for Hyperspectral and LiDAR Classification
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Metadaten
Titel
MADB-RemdNet for Few-Shot Learning in Remote Sensing Classification
verfasst von
Kun Wang
Yingying Wang
Zhiming Ding
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Spatial Data and Intelligence

Print ISBN: 978-981-9729-65-4

Electronic ISBN: 978-981-9729-66-1

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-2966-1_19

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