Article
Version 1
Preserved in Portico This version is not peer-reviewed
Invariant Attribute-driven Binary Bi-branch Classification for Hyperspectral and LiDAR Images
Version 1
: Received: 13 July 2023 / Approved: 14 July 2023 / Online: 17 July 2023 (14:09:49 CEST)
A peer-reviewed article of this Preprint also exists.
Zhang, J.; Lei, J.; Xie, W.; Li, D. Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images. Remote Sens. 2023, 15, 4255. Zhang, J.; Lei, J.; Xie, W.; Li, D. Invariant Attribute-Driven Binary Bi-Branch Classification of Hyperspectral and LiDAR Images. Remote Sens. 2023, 15, 4255.
Abstract
Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.
Keywords
Invariant Graph Convolutional Network (GCN); Convolutional Neural Network (CNN); Binary quantization; Hyperspectral image (HSI) classification
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment