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...
Spectral imaging extends the usefulness of spectroscopy by combining spectroscopy with imaging, thereby providing both spectral and spatial information. Spectral imaging requires sensitive detectors and powerful computers to enable fast processing of images. Its use has expanded from remote sensing for both...
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 ...
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...
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 ...
multispectral point clouds; land cover classification; spatial–spectral information; deep spatial graph convolution network; adaptive spectral residuals1. Introduction Navigation is a field of immense importance in modern society, requiring the integration of various disciplines, such as cartography, ...
The 3D-CNN uses three-dimensional convolution to directly learn the spectral and spatial information of HSIs [47] simultaneously. Therefore, the 3D-CNN is more suitable for HSI few-shot classification. However, the traditional 3D convolution kernel is usually fixed in size, which makes it ...
Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of...