spectral-spatial weightingSparse unmixing has made great progress in hyperspectral unmixing recently. To improve the unmixing accuracy, spatial information has been widely added to the unmixing model. However, due to the complexity of ground material mixing, the performance of the same model in ...
整个操作在Transformer编码器的块中执行,其中每个Transformer块由光谱和空间形态特征提取块和残差多头交叉注意块组成。 Spectral Morph和Spatial Morph两层的输出以通道形式(X′patch)与Xcls级联,以生成整个形态块的最终输出。来自光谱和空间形态块的输出通道是输入Xpatch的一半,因此,在将两者concatenate之后,通道的数量变得...
1. 研究并理解Spectral-Spatial Feature Tokenization的基本概念 Spectral-Spatial Feature Tokenization是一种技术,用于从高光谱图像中提取光谱和空间特征,并将这些特征转换为适合Transformer模型处理的令牌(tokens)。这涉及到对图像进行预处理,以便有效地捕捉图像中的光谱信息和空间关系。 2. 研究Transformer模型在图像处理中...
Method 1.Spectral–Spatial Feature Extraction:将经过PCA降维后的特征图{(m×n×l)->(m×n×b)}经过一个3-D卷积层和一个2-D卷积层进行初初始特征编码。 2.Gaussian-W eighted Feature T okenizer:通过两层卷积运算提取的特征携带光谱和空间信息,但不能充分描述地面物体的特征。因此,特征图被进一步定义为语...
Spectral-Spatial FeatureTransformerHyperspectral Unmixing (HU) plays a crucial role in advancing hyperspectral image (HSI) analysis. Its goal is to decompose mixed pixels into distinct spectral signatures, called endmembers, and to estimate the fractional abundances of each endmember across the image. ...
"Spectral-Spatial-RF-Pulse-DesignThis软件包包含了用于设计谱空间RF脉冲(也称为空间谱RF脉冲)的Matlab函数,适用于磁共振波谱学和成像应用。这些函数提供了灵活的工具,可用于优化RF脉冲的频谱和空间特性,以实现更好的成像和波谱选择性。通过该软件包,用户可以根据特定需求设计定制的RF脉冲,提高磁共振成像和波谱学的...
mamba_ssm is needed to be installed Code for the remote sensing paper 'Spectral-Spatial Mamba for Hyperspectral Image Classification'. Digital Object Identifier https://doi.org/10.3390/rs16132449 or arxiv: https://arxiv.org/abs/2404.18401About...
classifiers. Spatial information provides additional discriminant information related to the shape and size of different structures, which—if properly exploited—leads to more accurate classification maps [9]. Spectral–spatial classification methods can be generally divided into two categories. ...
Hyperspectral images contain hundreds of bands, with rich spectral and spatial information [1]. They have been explored in many areas, such as HSI classification [2], geological survey [3], biomedicine [4]. In particular, the HSI classification task is the basis of HSI analysis. It allocate...
可以看出,方法的核心在于SSMA模块,即spatial-spectral multi-head self-attention,该模块结构如下图所示: 其实原理也非常简单,图中画的非常清楚了。需要注意的是,在 spatial attention 前有一个 shift operation,论文中说是在X,Y方向分别 shift 的尺寸为 M/2 个像素,代码为: shifted_x = torch.roll(x, shifts...