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CMR-net: A cross modality reconstruction network for multi-modality remote sensing classification
Huiqing Wang1,2, Huajun Wang1, Lingfeng Wu1
1School of Geophysics, Chengdu University of Technology, Chengdu, Sichuan, China.
Plos One
|June 25, 2024
Summary
A new deep learning model, CMR-Net, effectively classifies multi-modality remote sensing (RS) data by integrating features from different sources. This approach enhances surface material identification in geoscience applications.
Area of Science:
- Geoscience and Remote Sensing (RS)
- Deep Learning Applications
Background:
- Surface material classification is crucial in geoscience and RS.
- Classifying multi-modality RS data remains challenging despite deep learning advancements.
Purpose of the Study:
- To propose a novel deep learning architecture for multi-modality RS image classification.
- To enhance feature fusion and information exchange between different data modalities.
Main Methods:
- Developed CMR-Net, a convolutional neural network (CNN) architecture.
- Introduced a cross modality reconstruction (CMR) module for feature fusion.
- Validated on hyperspectral (HS)/LiDAR (Houston2013) and HS/synthetic aperture radar (SAR) (Berlin) datasets.
Main Results:
- CMR-Net demonstrated superior performance in multi-modality RS data classification.
- The CMR module effectively integrated features from diverse data sources.
- Experimental results confirmed the model's effectiveness against state-of-the-art methods.
Conclusions:
- CMR-Net offers an effective solution for classifying multi-modality RS data.
- The proposed approach advances feature integration techniques in remote sensing.
- This work contributes to improved surface material identification using combined RS data.
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