To address these issues, we proposed a discriminative broad graph convolution network for hyperspectral image classification (DBGCN). Firstly, we use multiple edge preserving filters to extract spatial spectral features, and then use PCA to fuse the spatial spectral joint features obtained by edge ...
The spatial resolution of satellite remote-sensing systems is too low to identify many objects by their shape or spatial detail. In some cases, it is possible to identify such objects by spectral measurements. There has, therefore, been great interest in measuring the spectral signatures of surfac...
Specifically, a weighted fusion of spectral transformer and spatial self-attention (WFSS) is designed to achieve the multi-scale fusion of spectral and spatial connections, which further improves the model’s robustness. Comprehensive experiments on three benchmarks show that the WFSS framework has ...
The composite would represent the convolution, Eq. (3-2), of the sensor spatial response function and the pier radiance profile, sampled at an interval much finer than the pixel GSI. Just such an analysis is used later in this chapter to evaluate the ALI and QuickBird sensor spatial ...
Intentionally, this simple model does not include any properties of optics, sophisticated models of light-matter interactions, and spatial components (focus, sample surface, etc.). The aim of the method is to describe an observed object in the best way, with minimal assumptions on its nature or...
The key value of spectral (Fourier) domain interferometry (SDI) is its ability to encode spatial or temporal data into the spectrum at the interferometer output. The applications of SDI are wide-spread, encompassing spectroscopy1, astronomy2 and medical studies3 such as ophthalmology, where the la...
One is to perform a kernel convolution in the spa- tial domain and the other is to utilize the Discrete Fourier Transform (DFT) for filtering in the frequency domain. Ac- cording to the convolution theorem [26], the results of vi- sual feature processi...
Reverse Fourier transformwhich provides convolution outputs in the spatial graph signal domain via\(y = U Q \). The above formulation resembles that of [3,4], with the difference that we build thek-NN graph during runtime, computing its Laplacian and pooling hierarchy on the fly, so that ...
Spectral–spatial discriminative broad graph convolution networks for hyperspectral image classificationdoi:10.1007/s13042-022-01680-xGraph convolutional neural networkBroad learning systemPrincipal component analysisHyperspectral image classificationInternational Journal of Machine Learning and Cybernetics - Graph ...
First, convolution-based dimensionality reduction is performed to refine redundant spectral bands and save computational costs. Afterwards, the backbone of the network integrates the spatial CNN branch and the spectral Transformer branch into a dual-branch network structure to extract spatial context ...