CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification.
PLoS One
; 19(6): e0304999, 2024.
Article
in En
| MEDLINE
| ID: mdl-38917124
ABSTRACT
In recent years, the classification and identification of surface materials on earth have emerged as fundamental yet challenging research topics in the fields of geoscience and remote sensing (RS). The classification of multi-modality RS data still poses certain challenges, despite the notable advancements achieved by deep learning technology in RS image classification. In this work, a deep learning architecture based on convolutional neural network (CNN) is proposed for the classification of multimodal RS image data. The network structure introduces a cross modality reconstruction (CMR) module in the multi-modality feature fusion stage, called CMR-Net. In other words, CMR-Net is based on CNN network structure. In the feature fusion stage, a plug-and-play module for cross-modal fusion reconstruction is designed to compactly integrate features extracted from multiple modalities of remote sensing data, enabling effective information exchange and feature integration. In addition, to validate the proposed scheme, extensive experiments were conducted on two multi-modality RS datasets, namely the Houston2013 dataset consisting of hyperspectral (HS) and light detection and ranging (LiDAR) data, as well as the Berlin dataset comprising HS and synthetic aperture radar (SAR) data. The results demonstrate the effectiveness and superiority of our proposed CMR-Net compared to several state-of-the-art methods for multi-modality RS data classification.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neural Networks, Computer
/
Remote Sensing Technology
Language:
En
Journal:
PLoS One
Journal subject:
CIENCIA
/
MEDICINA
Year:
2024
Document type:
Article
Affiliation country: