Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification

Shah, Chiranjibi; Du, Qian; Xu, Yan

doi:10.3390/rs14030716

Cited by 43 publications

(13 citation statements)

References 53 publications

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“…In addition, many schemes were proposed for the optimal design of network structures in the convolutional neural networks. For example, Shah et al [ 31 ] proposed to use TabNet with spatial attention to extract the spatial information for hyperspectral image classification, in which 2D Convolutional Neural Networks (CNN) were incorporated into an attention converter for the spatial soft feature selection. Additionally, the dynamic texture smoothing approach was used to build the structural contour during the pre-processing step, where the matrix was utilized via extracting features.…”

Section: Related Workmentioning

confidence: 99%

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Liu

et al. 2023

Sensors

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Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.

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Section: Related Workmentioning

confidence: 99%

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Liu

et al. 2023

Sensors

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“…As stated at the beginning of Section 3, the HAD task was reckoned as a matrix decomposition problem, and it was transformed into Equation (3). With D B and D A constructed in Section 3.3.1, to solve the LRSR problem as (3), two auxiliary variables, J and L, are introduced to make the objective function separable.…”

Section: Low Rank and Sparse Representationmentioning

confidence: 99%

“…Hyperspectral images (HSIs), which have the characteristics of a wide spectral range and high spectral resolution, are widely utilized to discriminate physical properties of different materials [1]. Benefitting from the rich spectral information, HSIs are active in the field of image classification [2,3], hyperspectral unmixing [4,5], band selection [6,7], anomaly detection [8,9] and target detection [10,11]. Among these applications, hyperspectral anomaly detection (HAD), aiming to excavate the pixels with significant spectral difference relative to surrounding pixels [12], attracts particular interest.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

et al. 2022

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Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.

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“…Recently, structural profiles have received more attention in the hyperspectral image classification community [ 6 , 19 , 31 , 32 ]. For example, Shah et al used structural profiles in the preprocessing stage to remove texture details, greatly improving the classification performance of the classifier [ 31 ]. Liang et al developed a multi-view structural profile to characterize complex spectral–spatial features of hyperspectral images [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

Wang

Zeng²,

Duan³

et al. 2022

Sensors

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Hyperspectral image classification has received a lot of attention in the remote sensing field. However, most classification methods require a large number of training samples to obtain satisfactory performance. In real applications, it is difficult for users to label sufficient samples. To overcome this problem, in this work, a novel multi-scale superpixel-guided structural profile method is proposed for the classification of hyperspectral images. First, the spectral number (of the original image) is reduced with an averaging fusion method. Then, multi-scale structural profiles are extracted with the help of the superpixel segmentation method. Finally, the extracted multi-scale structural profiles are fused with an unsupervised feature selection method followed by a spectral classifier to obtain classification results. Experiments on several hyperspectral datasets verify that the proposed method can produce outstanding classification effects in the case of limited samples compared to other advanced classification methods. The classification accuracies obtained by the proposed method on the Salinas dataset are increased by 43.25%, 31.34%, and 46.82% in terms of overall accuracy (OA), average accuracy (AA), and Kappa coefficient compared to recently proposed deep learning methods.

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Enhanced TabNet: Attentive Interpretable Tabular Learning for Hyperspectral Image Classification

Cited by 43 publications

References 53 publications

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Table Tennis Track Detection Based on Temporal Feature Multiplexing Network

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

Multi-Scale Superpixel-Guided Structural Profiles for Hyperspectral Image Classification

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