Aerial Hyperspectral Remote Sensing Application Dataset in XiongAn (Matiwan Village) of Hebei Province of China

CEN, Yi; ZHANG, Lifu*; ZHANG, Xia; WANG, Yueming; QI, Wenchao; TANG, Senlin; Zhang, Peng

doi:10.3974/geodb.2021.01.02.v1

Cited by 11 publications

(12 citation statements)

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“…We evaluate our proposed method on three multispectral remote sensing datasets (Chikusei [ 49 ], Washington DC Mall [ 50 ] and XiongAn [ 51 ]) and two hyperspectral remote sensing datasets (HyRANK [ 52 ] and GF2Hyper [ 53 ]).…”

Section: Experimental Results and Analysismentioning

confidence: 99%

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

Zhu¹,

Wang

et al. 2023

Sensors

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Accurate hyperspectral remote sensing information is essential for feature identification and detection. Nevertheless, the hyperspectral imaging mechanism poses challenges in balancing the trade-off between spatial and spectral resolution. Hardware improvements are cost-intensive and depend on strict environmental conditions and extra equipment. Recent spectral imaging methods have attempted to directly reconstruct hyperspectral information from widely available multispectral images. However, fixed mapping approaches used in previous spectral reconstruction models limit their reconstruction quality and generalizability, especially dealing with missing or contaminated bands. Moreover, data-hungry issues plague increasingly complex data-driven spectral reconstruction methods. This paper proposes SpectralMAE, a novel spectral reconstruction model that can take arbitrary combinations of bands as input and improve the utilization of data sources. In contrast to previous spectral reconstruction techniques, SpectralMAE explores the application of a self-supervised learning paradigm and proposes a masked autoencoder architecture for spectral dimensions. To further enhance the performance for specific sensor inputs, we propose a training strategy by combining random masking pre-training and fixed masking fine-tuning. Empirical evaluations on five remote sensing datasets demonstrate that SpectralMAE outperforms state-of-the-art methods in both qualitative and quantitative metrics.

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Section: Experimental Results and Analysismentioning

confidence: 99%

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

Zhu¹,

Wang

et al. 2023

Sensors

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“…The hyperspectral image is collected by the high resolution special aviation system full spectrum multimodal imaging spectrometer developed by Shanghai Institute of Technical Physics of the Chinese Academy of Sciences ( Cen et al., 2020 ). Referring to the synchronously measured ground and atmospheric data, the pseudo color image about the reflectivity of various surface coverage types is obtained through geometric, radiometric and atmospheric correction.…”

Section: Methodsmentioning

confidence: 99%

A vegetation classification method based on improved dual-way branch feature fusion U-net

Jiang²,

Peng³

et al. 2022

Front. Plant Sci.

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Aiming at the problems of complex structure parameters and low feature extraction ability of U-Net used in vegetation classification, a deep network with improved U-Net and dual-way branch input is proposed. Firstly, The principal component analysis (PCA) is used to reduce the dimension of hyperspectral remote sensing images, and the effective bands are obtained. Secondly, the depthwise separable convolution and residual connections are combined to replace the common convolution layers of U-Net for depth feature extraction to ensure classification accuracy and reduce the complexity of network parameters. Finally, normalized difference vegetation index (NDVI), gray level co-occurrence matrix (GLCM) and edge features of hyperspectral remote sensing images are extracted respectively. The above three artificial features are fused as one input, and PCA dimension reduction features are used as another input. Based on the improved U-net, a dual-way vegetation classification model is generated. Taking the hyperspectral remote sensing image of Matiwan Village, Xiong’an, Beijing as the experimental object, the experimental results show that the precision and recall of the improved U-Net are significantly improved with the residual structure and depthwise separable convolution, reaching 97.13% and 92.36% respectively. In addition, in order to verify the effectiveness of artificial features and dual-way branch design, the accuracy of single channel and the dual-way branch are compared. The experimental results show that artificial features in single channel network interfere with the original hyperspectral data, resulting in reduction of the recognition accuracy. However, the accuracy of the dual-way branch network has been improved, reaching 98.67%. It shows that artificial features are effective complements of network features.

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“…In addition to datasets mentioned above, there remains several datasets for hyperspectral image classification, e.g., WashingtonDC dataset [22], Houston 2013 [23], WHU-Hi dataset [24], Curprite dataset [25], Matiwan Village dataset [25] et. al..…”

Section: Other Datasetsmentioning

confidence: 99%

SDFC dataset: a large-scale benchmark dataset for hyperspectral image classification

Sun

Zhang

et al. 2023

Opt Quant Electron

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Hyperspectral image (HSI) classification plays an important role in a wide range of remote sensing applications in military and civilian fields. During past decades, significant efforts have been made on developing datasets and introducing novel approaches to promote HSI classification, such that promising classification performance has been achieved. However, existing datasets generally pose following issues, including the limited categories and annotated samples, the lack of sample diversity, as well as the low spatial resolution. These limitations severely restrict the development and evaluation of data-driven models, especially SDFC Dataset: Benchmark Dataset for HSI Classification deep neural network-based ones. In recent years, advances in imaging spectroscopy provide us the opportunity to obtain the hyperspectral image data with high spectral and spatial resolution, therefore, in this paper, we contribute a large-scale benchmark dataset for conducting hyperspectral image classification to address issues raised by existing datasets, noted as ShanDongFeiCheng (SDFC). The proposed SDFC is characterized by 1) the large-scale annotated samples with diverse categories; 2) the high spatial resolution; and 3) the high intra-class variance yet relatively low inter-class variance, making the HSI classification task much more challenging on it. We evaluated 10 classic traditional and deep neural network-based models on SDFC, of which the results can be regarded as useful baselines for further experiments. Moreover, given the state-of-the-art performance of SpectralNet, we selected it as the representation method, and evaluated it across datasets to analyze the difference effects on the classification model induced by different datasets. The comprehensive review and analysis of representative classification models on both existing and proposed datasets demonstrate the advantages and challenges of our proposed dataset, and provide promising perspectives for future HSI classification studies.

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Aerial Hyperspectral Remote Sensing Application Dataset in XiongAn (Matiwan Village) of Hebei Province of China

Cited by 11 publications

References 0 publications

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

A vegetation classification method based on improved dual-way branch feature fusion U-net

SDFC dataset: a large-scale benchmark dataset for hyperspectral image classification

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