Skip to content

Latest commit

 

History

History
127 lines (100 loc) · 11.1 KB

README.md

File metadata and controls

127 lines (100 loc) · 11.1 KB
Raw

PWC

PWC

PWC

PWC

[TGRS 2023] Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification

Mingsong Li, Wei Li, Yikun Liu, Yuwen Huang, and Gongping Yang

Time Lab, SDU ; BIT

This repository is the official implementation of our paper: Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing (TGRS) 2023.

Contents

  1. Brief Introduction
  2. Environment
  3. Datasets and File Hierarchy
  4. Implementations of Compared Methods
  5. Citation
  6. License and Acknowledgement

Brief Introduction

For the abundant spectral and spatial information recorded in hyperspectral images (HSIs), fully exploring spectral-spatial relationships has attracted widespread attention in hyperspectral image classification (HSIC) community. However, there are still some intractable obstructs. For one thing, in the patch-based processing pattern, some spatial neighbor pixels are often inconsistent with the central pixel in land-cover class. For another thing, linear and nonlinear correlations between different spectral bands are vital yet tough for representing and excavating. To overcome these mentioned issues, an adaptive mask sampling and manifold to Euclidean subspace learning (AMS-M2ESL) framework is proposed for HSIC. Specifically, an adaptive mask based intra-patch sampling (AMIPS) module is firstly formulated for intra-patch sampling in an adaptive mask manner based on central spectral vector oriented spatial relationships. Subsequently, based on distance covariance descriptor, a dual channel distance covariance representation (DC-DCR) module is proposed for modeling unified spectral-spatial feature representations and exploring spectral-spatial relationships, especially linear and nonlinear interdependence in spectral domain. Furthermore, considering that distance covariance matrix lies on the symmetric positive definite (SPD) manifold, we implement a manifold to Euclidean subspace learning (M2ESL) module respecting Riemannian geometry of SPD manifold for high-level spectral-spatial feature learning. Additionally, we introduce an approximate matrix square-root (ASQRT) layer for efficient Euclidean subspace projection. Extensive experimental results on three popular HSI datasets with limited training samples demonstrate the superior performance of the proposed method compared with other state-of-the-art methods. The source code is available at https://github.com/lms-07/AMS-M2ESL.

AMS-M2ESL Framework

framework

Environment

  • The software environment is Ubuntu 18.04.5 LTS 64 bit.
  • This project is running on a single Nvidia GeForce RTX 3090 GPU based on Cuda 11.0.
  • We adopt Python 3.8.5, PyTorch 1.10.0+cu111.
  • The py+torch combination may not be limited by our adopted one.

Datasets and File Hierarchy

Three representative HSI datasets are adopted in our experiments, i.e., Indian Pines (IP), University of Pavia (UP), and University of Houston 13 (UH). The first two datasets could be accessed through link1, and the UH dataset through link2. Our project is organized as follows:

AMS-M2ESL
|-- process_xxx     // main files 1) dl for the proposed model 2) cls_c_model 
|                      for the classic compared model, SVM 3) dl_c_model for eight 
|                      dl based compared methods 4) disjoint for the 
|                      disjoint dataset (UH) 5) m_scale for the multiscale model, MCM-CNN
|-- c_model         // eight deep learning based compared methods
|-- data                    
|   |-- IP
|   |   |-- Indian_pines_corrected.mat
|   |   |-- Indian_pines_gt.mat
|   |-- UP
|   |   |-- PaviaU.mat
|   |   |-- PaviaU_gt.mat
|   |-- HU13_tif
|   |   |--Houston13_data.mat
|   |   |--Houston13_gt_train.mat
|   |   |--Houston13_gt_test.mat
|-- model           // the proposed method
|-- output
|   |-- cls_maps    // classification map visualizations 
|   |-- results     // classification result files
|-- src             // source files
|-- utils           // data loading, processing, and evaluating
|-- visual          // cls maps visual

Implementations of Compared Methods

For comparisons, our codebase also includes related compared methods.

Citation

Please kindly cite our work if this work is helpful for your research.

[1] M. Li, W. Li, Y. Liu, Y. Huang and G. Yang, "Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning With Distance Covariance Representation for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023, Art no. 5508518.

BibTex entry:

@article{li2023adaptive,
  title={Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification},
  author={Li, Mingsong and Li, Wei and Liu, Yikun and Huang, Yuwen and Yang, Gongping},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2023},
  volume={61},
  number={},
  pages={1-18},
  publisher={IEEE},
}

Contact information

If you have any problem, please do not hesitate to contact us msli@mail.sdu.edu.cn.

License and Acknowledgement