高光谱分类算法

相关运算

软阈值

2015 Supervised-Spectral-spatial-Hyperspectral-Image-Classification-with-Weighted-Markov-Random-Fields

(1)

（2）使用ADMM算法处理优化问题

2017 Dissimilarity-Weighted Sparse Representation for Hyperspectral Image Classification

通过考虑基于待分类像素与字典的像素局部信息，提出了DWSRC（dissimilarity-weighted sparse representation-based classifier）分类器。

Algorithm 1 Proposed DWSRC for HSIs
Input: A initial dictionary of training samples for class; a test pixel set ; sparsity regularization
For each test pixel do
    1) Calculate the weight matrix between and each dictionary atom via Gaussian kernel distance with a suitable distance metric;
    2) Generate the locality-constrained dictionary set  , where 
    3) Sparsely code over the new weighted dictionary  via 1-norm minimization according to

    4) Identify the final class of   with the minimal residual rule according to

end for
Output: An 2-D map that records the labels of the test sample set

2015-Extreme-Learning-Machine-With-Composite-Kernels-for-Hyperspectral-Image-Classification

（1）提取每一个像素的空间特征和光谱特征

The spectral feature vector  is the original , which consists of spectral reflection values across all bands.
The spatial feature vector  i is extracted from the local spatial neighborhood of pixel  and defined as the mean of pixels in the spatial neighborhood of  in this paper.

（2）计算ELM隐含层输出矩阵和

（3）输出分类结果

计算系数

Local adaptive joint sparse representation for hyperspectral image classification

2019

此文提出了LAJSR算法.