Throughout the history of remote sensing research, numerous methods for hyperspectral image analysis have been presented. Depending on the spatial resolution of the images, specific mathematical models must be designed to effectively analyze the imagery. Some of these models operate at a sub-pixel ...
3D-CNN(《Deep feature extraction and classification of hyperspectral images based on convolutional neural networks》), Gabor-CNN,带有像素对特征的CNN (CNN-PPF),暹罗CNN (S-CNN) , 3D-GAN和深度特征融合网络(DFFN),用于HSI分类。
'1 Spatial Peak-Aware Collaborative Representation for Hyperspectral Imagery Classification 1' 【Code】 '1 Hyperspectral Image Classification via JCR and SVM Models With Decision Fusion 1'【Code】 '1 Spectral-Spatial Hyperspectral Classification via Structural-Kernel Collaborative Representation 1'【Code】 ...
Hyperspectral Image Classification Based On Spatial and Spectral Kernels Generation Network With the widespread use of deep learning methods, more and more classification models based on hyperspectral images (HSI) have been continuously proposed. ... W Ma,H Ma,H Zhu,... - 《Information Sciences》...
1999). The results shown in this work indicate that neural network models are able to find clusters of closely related hyperspectral signatures, and thus can be used as a powerful tool to achieve the desired classification. 1 Departamento de Informática, Universidad de Extremadura, Avda. de la...
In recent years, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, the existing network models have higher model complexity and require more time consumption. Traditional hyperspectral image classification methods te...
Advances in Hyperspectral Image Classification Based on Convolutional Neural Networks: A Review deep learning models,convolutional neural networks(CNNs)have shown huge success and offered great potential to yield high performance in HSI classification... S Bera,VK Shrivastava,SC Satapathy - 工程与科学...
(DBN) was applied to extract features and classification results were obtained by logistic regression classifier. For these models, inputs are high-dimensional vectors. Therefore, to learn the spatial feature from HSIs, an alternative method is flattening a local image patch into a vector and ...
In recent years, the hyperspectral classification algorithm based on deep learning has received widespread attention, but the existing network models have higher model complexity and require more time consumption. In order to further improve the accuracy of hyperspectral image classification and reduce mode...
Deep neural network has been extensively applied to hyperspectral image (HSI) classification and shown promising performance recently. However, those popular deep learning models scarcely consider low-rank features of high-dimensional HS... F Ye,Z Chen - 《Journal of Physics Conference》 被引量: 0...