Hyperspectral Image Segmentation

Exploring the Relationship Between Center and Neighborhoods: Central Vector Oriented Self-Similarity Network for Hyperspectral Image Classification

Mingsong Li, Yikun Liu, Guangkuo Xue, Yuwen Huang, Gongping Yang•Fri Mar 31 2023

To mine the spectral-spatial information of target pixel in hyperspectral image classification (HSIC), convolutional neural network (CNN)-based models widely adopt patch-based input pattern, where a patch represents its central pixel and the neighbor pixels play auxiliary roles in the classification process. However, compared to the central pixel, its neighbor pixels often have different contributions for classification. Although many existing patch-based CNNs could adaptively emphasize the spatial neighbor information, most of them ignore the latent relationship between the center pixel and its neighbor pixels. Moreover, efficient spectral-spatial feature extraction has been a difficult yet vital topic for HSIC. To address the mentioned problems, a central vector oriented self-similarity network (CVSSN) is proposed for HSIC. Specifically, based on two similarity measures, we firstly design an adaptive weight addition based spectral vector self-similarity module (AWA-SVSS) in input space and a Euclidean distance based feature vector self-similarity module (ED-FVSS) in feature space to fully mine the central vector oriented spatial relationships. Besides, a spectral-spatial information fusion module (SSIF) is formulated as a new pattern to fuse the central 1D spectral vector and the corresponding 3D patch for efficient spectral-spatial feature learning of the subsequent modules. Moreover, we implement a channel spatial separation convolution module (CSS-Conv) and a scale information complementary convolution module (SIC-Conv) for efficient spectral-spatial feature learning. Extensive experimental results on four popular HSI data sets demonstrate the effectiveness and efficiency of the proposed method compared with other state-of-the-art methods. The source code is available at https://github.com/lms-07/CVSSN

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Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning With Distance Covariance Representation for Hyperspectral Image Classification

Wei Li, Yikun Liu, Yuwen Huang, Gongping Yang, Mingsong Li•Sat Dec 31 2022

For the abundant spectral and spatial information recorded in hyperspectral images (HSIs), fully exploring spectral–spatial relationships has attracted widespread attention in the HSI 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 the Euclidean subspace learning (AMS-M2ESL) framework is proposed for HSIC. Specifically, an adaptive mask-based intrapatch sampling (AMIPS) module is first formulated for intrapatch sampling in an adaptive mask manner based on central spectral vector-oriented spatial relationships. Subsequently, based on the 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 the spectral domain. Furthermore, considering that the distance covariance matrix lies on the symmetric positive definite (SPD) manifold, we implement an M2ESL module respecting the Riemannian geometry of the 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.

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Exploring the Relationship Between Center and Neighborhoods: Central Vector Oriented Self-Similarity Network for Hyperspectral Image Classification

Hyperspectral image segmentation: a preliminary study on the Oral and Dental Spectral Image Database (ODSI-DB)

Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning With Distance Covariance Representation for Hyperspectral Image Classification