Hyperspectral Image Classification Based on 3D Coordination Attention Mechanism Network

Shi, Cuiping; Liao, Diling; Zhang, Tianyu; Wang, Liguo

doi:10.3390/rs14030608

Cited by 21 publications

(5 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…h) Include attention modules: Inspired by the success of attention modules [161], [185], [186]. The main focus of application in RS data has been to perform temporal attention [1], [15], [36], [74], [155], [157], [165], [182], [187], [188].…”

Section: Modeling Considerationsmentioning

confidence: 99%

Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications

Mena,

Arenas,

Nuske

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibration or deterioration. A great variety of deep learning models have been applied to fuse the information from these multiple views, known as deep multiview or multi-modal fusion learning. However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques. This article gathers works on multi-view fusion for Earth observation by focusing on the common practices and approaches used in the literature. We summarize and structure insights from several different publications concentrating on unifying points and ideas. In this manuscript, we provide a harmonized terminology while at the same time mentioning the various alternative terms that are used in literature. The topics covered by the works reviewed focus on supervised learning with the use of neural network models. We hope this review, with a long list of recent references, can support future research and lead to a unified advance in the area.

show abstract

Section: Modeling Considerationsmentioning

confidence: 99%

Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications

Mena,

Arenas,

Nuske

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Shi et al [ 25 ] presented a model namely the 3D coordination attention mechanism (3DCAM). This attention process could not attain the HIS's spatial position in both vertical and horizontal ways.…”

Section: Literature Reviewmentioning

confidence: 99%

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

Reddy¹,

Harikiran²,

Enduri

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The classification technology of hyperspectral images (HSI) consists of many contiguous spectral bands that are often utilized for a various Earth observation activities, such as surveillance, detection, and identification. The incorporation of both spectral and spatial characteristics is necessary for improved classification accuracy. In the classification of hyperspectral images, deep learning has gained significant traction. This research analyzes how to accurately classify new HSI from limited samples with labels. A novel deep-learning-based categorization based on feature extraction and classification is designed for this purpose. Initial extraction of spectral and spatial information is followed by spectral and spatial information integration to generate fused features. The classification challenge is completed using a compressed synergic deep convolution neural network with Aquila optimization (CSDCNN-AO) model constructed by utilising a novel optimization technique known as the Aquila Optimizer (AO). The HSI, the Kennedy Space Center (KSC), the Indian Pines (IP) dataset, the Houston U (HU) dataset, and the Salinas Scene (SS) dataset are used for experiment assessment. The sequence testing on these four HSI-classified datasets demonstrate that our innovative framework outperforms the conventional technique on common evaluation measures such as average accuracy (AA), overall accuracy (OA), and Kappa coefficient (k). In addition, it significantly reduces training time and computational cost, resulting in enhanced training stability, maximum performance, and remarkable training accuracy.

show abstract

“…The attentional mechanism has been proved helpful for computervision tasks [41,42]. The performance of computer-vision tasks is effectively improved by combining the attention mechanism and deep networks; therefore, the attention mechanism has been widely used in computer-vision fields, such as image classification and semantic segmentation in recent years [43][44][45][46]. At first, the attention mechanism was usually applied to convolutional neural networks.…”

Section: Attention Mechanismmentioning

confidence: 99%

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Zhang

Liu

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Change detection determines and evaluates changes by comparing bi-temporal images, which is a challenging task in the remote-sensing field. To better exploit the high-level features, deep-learning-based change-detection methods have attracted researchers’ attention. Most deep-learning-based methods only explore the spatial–spectral features simultaneously. However, we assume the key spatial-change areas should be more important, and attention should be paid to the specific bands which can best reflect the changes. To achieve this goal, we propose the spatial–spectral joint attention network (SJAN). Compared with traditional methods, SJAN introduces the spatial–spectral attention mechanism to better explore the key changed areas and the key separable bands. To be more specific, a novel spatial-attention module is designed to extract the spatially key regions first. Secondly, the spectral-attention module is developed to adaptively focus on the separable bands of land-cover materials. Finally, a novel objective function is proposed to help the model to measure the similarity of learned spatial–spectral features from both spectrum amplitude and angle perspectives. The proposed SJAN is validated on three benchmark datasets. Comprehensive experiments have been conducted to demonstrate the effectiveness of the proposed SJAN.

show abstract

Hyperspectral Image Classification Based on 3D Coordination Attention Mechanism Network

Cited by 21 publications

References 58 publications

Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications

Common Practices and Taxonomy in Deep Multiview Fusion for Remote Sensing Applications

Hyperspectral Image Classification with Optimized Compressed Synergic Deep Convolution Neural Network with Aquila Optimization

A Spatial–Spectral Joint Attention Network for Change Detection in Multispectral Imagery

Contact Info

Product

Resources

About