Dual-Channel Residual Network for Hyperspectral Image Classification With Noisy Labels

Xu, Yong; Li, Zhaokui; Li, Wei; Du, Qian; Liu, Cuiwei; Fang, Zhuoqun; Zhai, Li

doi:10.1109/tgrs.2021.3057689

Cited by 43 publications

(31 citation statements)

References 28 publications

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“…(2) PSPNet-RL, which employed a robust loss proposed in [54] to alleviate the impact of noisy labels; (3) PSPNet trained by a training set collected outside the current data (we called it PSPNet-TF in this study); (4) the traditional PSPNet. The above-mentioned algorithms and the proposed MTNet were performed on the training set introduced in Section 3.3.2.…”

Section: Qualitative Results For Typical Regionsmentioning

confidence: 99%

“…(2) Generally, PSPNet-RL obtained better accuracies than the PSPNet, due to the introduced robust loss function. This means the robust loss proposed in [54] does have the ability of improving the robustness to noisy labels. However, as shown in Figures 6-8, this improvement in the classification accuracy is at the cost of decreasing the recognition ability of complex features.…”

Section: Quantitative Results For Typical Regionmentioning

confidence: 99%

“…[53] combined mixup entropy and Kullback-Leibler entropy to define a new loss function by fully utilizing the difference between them. A similar idea was performed in [54]. Ref.…”

Section: Noisy Label Problemmentioning

confidence: 86%

“…Ref. [54] proposed a novel dual-channel structure to improve the learning ability along with a noisy robust loss function constructed by reverse cross entropy and normalized cross entropy.…”

Section: Noisy Label Problemmentioning

confidence: 99%

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Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

et al. 2021

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Large-scale land-cover classification using a supervised algorithm is a challenging task. Enormous efforts have been made to manually process and check the production of national land-cover maps. This has led to complex pre- and post-processing and even the production of inaccurate mapping products from large-scale remote sensing images. Inspired by the recent success of deep learning techniques, in this study we provided a feasible automatic solution for improving the quality of national land-cover maps. However, the application of deep learning to national land-cover mapping remains limited because only small-scale noisy labels are available. To this end, a mutual transfer network MTNet was developed. MTNet is capable of learning better feature representations by mutually transferring pre-trained models from time-series of data and fine-tuning current data. An interactive training strategy such as this can effectively alleviate the effects of inaccurate or noisy labels and unbalanced sample distributions, thus yielding a relatively stable classification system. Extensive experiments were conducted by focusing on several representative regions to evaluate the classification results of our proposed method. Quantitative results showed that the proposed MTNet outperformed its baseline model about 1%, and the accuracy can be improved up to 6.45% compared with the model trained by the training set of another year. We also visualized the national classification maps generated by MTNet for two different time periods to quantitatively analyze the performance gain. It was concluded that the proposed MTNet provides an efficient method for large-scale land cover mapping.

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Section: Qualitative Results For Typical Regionsmentioning

confidence: 99%

Section: Quantitative Results For Typical Regionmentioning

confidence: 99%

“…[53] combined mixup entropy and Kullback-Leibler entropy to define a new loss function by fully utilizing the difference between them. A similar idea was performed in [54]. Ref.…”

Section: Noisy Label Problemmentioning

confidence: 86%

Section: Noisy Label Problemmentioning

confidence: 99%

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Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

et al. 2021

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“…For instance, Yang et al [41] implemented a deep CNN with two-branch architecture for HSI classification, in which low and mid-layers are pretrained on other data sources, with a twolayer MLP performing the final classification. Xu et al [42] proposed a novel dual-channel residual network for classifying HSI with noisy labels, which employs a noise-robust loss function to enhance model robustness and utilizes a single layer MLP for classification. To overcome this drawback, we adopt a weight sharing strategy in the proposed MLP-based architecture, which can lead to significant memory savings and will be detailed in the following Section.…”

Section: Input Layer Hidden Layers Output Layermentioning

confidence: 99%

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

2021

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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.

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Dictionary cache transformer for hyperspectral image classification

Zhou,

Zhang,

Zhang

et al. 2023

Appl Intell

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Dual-Channel Residual Network for Hyperspectral Image Classification With Noisy Labels

Cited by 43 publications

References 28 publications

Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

Transferable Deep Learning from Time Series of Landsat Data for National Land-Cover Mapping with Noisy Labels: A Case Study of China

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

Dictionary cache transformer for hyperspectral image classification

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