Hyper spectral imageHyperspectral image (HSI) classification is very important task having numerous applications in the remote sensing field. Many methods have been proposed in the recent years. Among them Convolutional Neural Networks (CNNs) based algorithms have shown higher performance. But these ...
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。
false color image 和 ground truth data: 2.University of Pavia:该图像的尺寸为610×340×115,空间分辨率为1.3 m/pixel,光谱覆盖范围为0.43 to 0.86 μm. 选取九个类进行实验,实验之前去除了12个噪声很大的波段。false color image 和 ground truth data: 3.AVIRIS Salinas:该图像有224个大小为512 × 217的...
Yu C, Zhao M, Song M, et al. Hyperspectral image classification method based on CNN architecture embedding with hashing semantic feature[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(6): 1866-1881. 摘要记录 在本文中,作者提出了一种嵌入提取哈希...
Hyperspectral image (HSI) classification draws a lot of attentions in the past decades. The classical problem of HSI classification mainly focuses on a sin... M Ye,W Zheng,H Lu,... - 《Int.j.wavelets Multiresolution Inf.process》 被引量: 0发表: 2017年 Robust hyperspectral image classifica...
摘要--针对HSI分类任务样本不足的问题,提出了一种deep few-shot小样本学习方法。该算法有三种新的策略: 利用深度残差三维卷积神经网络提取光谱-空间特征,降低标记的不确定性。 网络通过情景训练来学习一个度量空间,其中来自同一个类的样本比较接近,而来自不同类的样本比较远。
Hyperspectral image (HSI) classification is one of the most widely used methods for scene analysis from hyperspectral imagery. In the past, many different engineered features have been proposed for the HSI classification problem. In this paper, however, we propose a feature learning approach for hyp...
In this paper, we propose a hyperspectral image (HSI) classification method using spectral-spatial long short term memory (LSTM) networks. Specifically, for each pixel, we feed its spectral values in different channels into Spectral LSTM one by one to le
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. Tr
Hyperspectral Image (HSI) classification is one of the main research directions of remote sensing applications. With the high dimension, strong correlation, and a large amount of HSI data, conventional classification methods often have problems like computation complexity, low classification accuracy, and...