Adaptive Hash Attention and Lower Triangular Network for Hyperspectral Image Classification

Ge, Zixian; Cao, Guo; Zhang, Youqiang; Li, Xuesong; Shi, Hao; Fu, Peng

doi:10.1109/tgrs.2021.3075546

Cited by 17 publications

(16 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spatial attention module is made to suppress interference pixels in images and enhance attention to regions of interest 44 . The interference pixels at the edges of HSI patches can negatively impact the expressiveness of the extracted features 53 . The self-attention mechanism is a typical spatial attention module that has recently become well known in image processing.…”

Section: Related Workmentioning

confidence: 99%

“…44 The interference pixels at the edges of HSI patches can negatively impact the expressiveness of the extracted features. 53 The self-attention mechanism is a typical spatial attention module that has recently become well known in image processing. The self-attention mechanism captures long-range contextual information to obtain discriminative feature representations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Center-similarity spectral-spatial attention network for hyperspectral image classification

Zhang,

Liang,

Niu

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

Hyperspectral image (HSI) classification aims to assign labels to pixels to be classified. The high-dimensional form of HSI and the introduction of spatial information can introduce challenges such as redundant spectral bands and interference pixels. Recently, many methods based on convolutional neural networks and attention mechanisms have been employed to address these issues. However, existing methods often fail to adequately utilize spatial information and exhibit interference pixel diffusion, which may result in these methods being unable to extract discriminative features. In addition, convolutions are not capable of extracting robust features, which results in the poor robustness of recent methods when HSI is rotated. To address these challenges, a center-similarity spectral-spatial attention network model (CS 3 AN) is proposed. First, a center-similarity spectral attention (CSSpeA) module is proposed to exploit the input spatial information rationally to better reduce the impact of redundant bands. Next, a center-similarity rectified spatial attention (CSRSpaA) module that selectively weighs neighboring pixels based on interference pixels to prevent the diffusion phenomenon is provided. Second, a similarity spatial aggregation module is employed to extract robust spectral-spatial features. Finally, the robust features are input into the softmax to get the label. The validity of the CS 3 AN is validated on three different databases.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Center-similarity spectral-spatial attention network for hyperspectral image classification

Zhang,

Liang,

Niu

et al. 2024

J. Appl. Rem. Sens.

View full text Add to dashboard Cite

show abstract

“…By integrating the SaMLP and SeMLP, discriminative spectral-spatial features can be extracted from HSI cubes. In addition, we adopt the skip connection mechanism of [44] to enhance information exchange between layers, which has been demonstrated to be an effective strategy for modern neural architecture design [45][46][47]. The SaMLP and SeMLP have similar architecture and both consist of two fully connected layers and a non-linear activation, as shown in Figure 4.…”

Section: Ss-mlp Blockmentioning

confidence: 99%

SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

2021

View full text Add to dashboard Cite

Convolutional neural networks (CNNs) are the go-to model for hyperspectral image (HSI) classification because of the excellent locally contextual modeling ability that is beneficial to spatial and spectral feature extraction. However, CNNs with a limited receptive field pose challenges for modeling long-range dependencies. To solve this issue, we introduce a novel classification framework which regards the input HSI as a sequence data and is constructed exclusively with multilayer perceptrons (MLPs). Specifically, we propose a spectral-spatial MLP (SS-MLP) architecture, which uses matrix transposition and MLPs to achieve both spectral and spatial perception in global receptive field, capturing long-range dependencies and extracting more discriminative spectral-spatial features. Four benchmark HSI datasets are used to evaluate the classification performance of the proposed SS-MLP. Experimental results show that our pure MLP-based architecture outperforms other state-of-the-art convolution-based models in terms of both classification performance and computational time. When comparing with the SSSERN model, the average accuracy improvement of our approach is as high as 3.03%. We believe that our impressive experimental results will foster additional research on simple yet effective MLP-based architecture for HSI classification.

show abstract

“…To fully explore the discriminant features, Zhan et al innovated a three-direction spectral-spatial convolution neural network to improve the accuracy of change detection [44]. To eliminate redundant information and interclass interference, Ge et al designed an adaptive hash attention and lower triangular network for HSI classification [45]. Although these classification methods can extract deep joint spectral-spatial features, it is still inconvenient for them to focus more on discriminated feature regions and restrain the unnecessary information from plentiful spectral-spatial features.…”

Section: Introductionmentioning

confidence: 99%

A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification

et al. 2022

View full text Add to dashboard Cite

Due to end-to-end optimization characteristics and fine generalization ability, convolutional neural networks have been widely applied to hyperspectral image (HSI) classification, playing an irreplaceable role. However, previous studies struggle with two major challenges: (1) HSI contains complex topographic features, the number of labeled samples in different categories is unbalanced, resulting in poor classification for categories with few labeled samples; (2) With the deepening of neural network models, it is difficult to extract more discriminative spectral-spatial features. To address the issues mentioned above, we propose a discriminative spectral-spatial-semantic feature network based on shuffle and frequency attention mechanisms for HSI classification. There are four main parts of our approach: spectral-spatial shuffle attention module (SSAM), context-aware high-level spectral-spatial feature extraction module (CHSFEM), spectral-spatial frequency attention module (SFAM), and cross-connected semantic feature extraction module (CSFEM). First, to fully excavate the category attribute information, SSAM based on a “Deconstruction-Reconstruction” structure is designed, solving the problem of poor classification performance caused by an unbalanced number of label samples. Considering that deep spectral-spatial features are difficult to extract, CHSFEM and SFAM are constructed. The former is based on the “Horizontal-Vertical” structure to capture context-aware high-level multiscale features. The latter introduces multiple frequency components to compress channels to obtain more multifarious features. Finally, towards suppressing noisy boundaries efficiently and capturing abundant semantic information, CSFEM is devised. Numerous experiments are implemented on four public datasets: the evaluation indexes of OA, AA and Kappa on four datasets all exceed 99%, demonstrating that our method can achieve satisfactory performance and is superior to other contrasting methods.

show abstract

Adaptive Hash Attention and Lower Triangular Network for Hyperspectral Image Classification

Cited by 17 publications

References 63 publications

Center-similarity spectral-spatial attention network for hyperspectral image classification

Center-similarity spectral-spatial attention network for hyperspectral image classification

SS-MLP: A Novel Spectral-Spatial MLP Architecture for Hyperspectral Image Classification

A Discriminative Spectral-Spatial-Semantic Feature Network Based on Shuffle and Frequency Attention Mechanisms for Hyperspectral Image Classification

Contact Info

Product

Resources

About