Image recovery and recognition: a combining method of matrix norm regularisation

Wang, Weigang; Song, Wei; Wang, GuangYuan; Zeng, Guigen; Tian, Feng

doi:10.1049/iet-ipr.2018.5511

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…The second step is radiometric calibration, wherein the original DN values were converted into reflectance using the professional spectral software SpecView. The third step is smoothing and noise reduction, for which the classical Frobenius norm 2 method was used to retain the complete spectral space information and achieve data dimensionality reduction (Wang et al, 2019).…”

Section: Data Processingmentioning

confidence: 99%

Hyperspectral Image Classification in Desert Grassland Based on Three-Dimensional Deep Learning Model

Wang

ZHANG

et al. 2023

INMATEH

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Identification and classification of vegetation are the basis for grassland degradation monitoring, classification and quantification studies. Here, four deep learning models were used to classify the unmanned aerial vehicle (UAV) hyperspectral remote sensing images of desert grassland. VGG16 and ResNet18 achieved better image classification results for vegetation and bare soil, whereas three-dimensional (3D)-VGG16 and 3D-ResNet18, improved by 3D convolutional kernels, achieved better classification for vegetation, bare soil and small sample features in the images. The number of convolutional kernels, its size and batch size parameters of each model were optimised, and 3D-ResNet18-J had the best classification performance, with an overall classification accuracy of 97.74%. It achieved high precision and efficiency in classifying UAV hyperspectral remote sensing images of desert grassland.

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Section: Data Processingmentioning

confidence: 99%

Hyperspectral Image Classification in Desert Grassland Based on Three-Dimensional Deep Learning Model

Wang

ZHANG

et al. 2023

INMATEH

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“…However, the choice of dimensionality reduction method can affect the experimental results. In this experiment, the within-band variance-based combined with Frobenius norm 2 [48] (F-norm 2 ) algorithm was compared with a principal component analysis (PCA), a standard dimensionality reduction algorithm for hyperspectral images, to select the most accurate classification result among the initial network model processing methods.…”

Section: Waveband Processingmentioning

confidence: 99%

The Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Classification of Desert Grassland Plants in Inner Mongolia, China

Wang,

Bi,

et al. 2023

Applied Sciences

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In recent years, grassland ecosystems have faced increasingly severe desertification, which has caused continuous changes in the vegetation composition in grassland ecosystems. Therefore, effective research on grassland plant taxa is crucial to exploring the process of grassland desertification. This study proposed a solution by constructing a UAV hyperspectral remote sensing system to collect the hyperspectral data of various species in desert grasslands. This approach overcomes the limitations of traditional grassland survey methods such as a low efficiency and insufficient spatial resolution. A streamlined 2D-CNN model with different feature enhancement modules was constructed, and an improved depth-separable convolution approach was used to classify the desert grassland plants. The model was compared with existing hyperspectral classification models, such as ResNet34 and DenseNet121, under the preprocessing condition of data downscaling by combining the variance and F-norm2. The results showed that the model outperformed the other models in terms of the overall classification accuracy, kappa coefficient, and memory occupied, achieving 99.216%, 98.735%, and 16.3 MB, respectively. This model could effectively classify desert grassland species. This method provides a new approach for monitoring grassland ecosystem degradation.

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Kronecker component with robust low-rank dictionary for image denoising

Zhang

liu

2022

Displays

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Image recovery and recognition: a combining method of matrix norm regularisation

Cited by 6 publications

References 31 publications

Hyperspectral Image Classification in Desert Grassland Based on Three-Dimensional Deep Learning Model

Hyperspectral Image Classification in Desert Grassland Based on Three-Dimensional Deep Learning Model

The Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Classification of Desert Grassland Plants in Inner Mongolia, China

Kronecker component with robust low-rank dictionary for image denoising

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