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[HTML][HTML] Hyperspectral sensing of plant diseases: principle and methods
Pathogen infection has greatly reduced crop production. As the symptoms of diseases
usually appear when the plants are infected severely, rapid identification approaches are …
usually appear when the plants are infected severely, rapid identification approaches are …
Multiscale densely-connected fusion networks for hyperspectral images classification
Convolutional neural network (CNN) has demonstrated to be a powerful tool for
hyperspectral images (HSIs) classification. Previous CNN-based HSI classification methods …
hyperspectral images (HSIs) classification. Previous CNN-based HSI classification methods …
Extinction profiles fusion for hyperspectral images classification
An extinction profile (EP) is an effective spatial-spectral feature extraction method for
hyperspectral images (HSIs), which has recently drawn much attention. However, the …
hyperspectral images (HSIs), which has recently drawn much attention. However, the …
Multiscale superpixel-based hyperspectral image classification using recurrent neural networks with stacked autoencoders
This paper develops a novel hyperspectral image (HSI) classification framework by
exploiting the spectral-spatial features of multiscale superpixels via recurrent neural …
exploiting the spectral-spatial features of multiscale superpixels via recurrent neural …
Land cover classification based on the PSPNet and superpixel segmentation methods with high spatial resolution multispectral remote sensing imagery
Classifying land cover using high-resolution remote-sensing images is challenging. The
emergence of deep learning provides improved possibilities, but owing to the limitations of …
emergence of deep learning provides improved possibilities, but owing to the limitations of …
[HTML][HTML] A spectral spatial attention fusion with deformable convolutional residual network for hyperspectral image classification
Convolutional neural networks (CNNs) have exhibited excellent performance in
hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it …
hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it …
Hyperspectral image classification based on superpixel merging and broad learning system
F **e, R Wang, C **, G Wang - The Photogrammetric Record, 2024 - Wiley Online Library
Most spectral–spatial classification methods for hyperspectral images (HSIs) can achieve
satisfactory classification results. However, the common problem faced with these …
satisfactory classification results. However, the common problem faced with these …
Hyperspectral image classification based on spectral multiscale convolutional neural network
C Shi, J Sun, L Wang - Remote Sensing, 2022 - mdpi.com
In recent years, convolutional neural networks (CNNs) have been widely used for
hyperspectral image classification, which show good performance. Compared with using …
hyperspectral image classification, which show good performance. Compared with using …
Hyperspectral image classification via spatial window-based multiview intact feature learning
Due to the high dimensionality of hyperspectral images (HSIs), more training samples are
needed in general for better classification performance. However, surface materials cannot …
needed in general for better classification performance. However, surface materials cannot …
A deep manifold learning approach for spatial-spectral classification with limited labeled training samples
One major challenge of designing deep learning systems for hyperspectral data
classification is the lack of labeled training samples. Inspired by recent manifold learning …
classification is the lack of labeled training samples. Inspired by recent manifold learning …