Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based chemometric methods is limited and often reduced to grid searching. Previously, the authors introduced Kernel Flows (KF) to learn kernel parameters for Kernel Partial Least-Squares (K-PLS) regression. KF is easy to implement and helps minimize overfitting. In cases of high collinearity between spectra and biogeophysical quantities in spectroscopy, simpler methods like Principal Component Regression (PCR) may be more suitable. In this study, we propose a new KF-type approach to optimize Kernel Principal Component Regression (K-PCR) and test it alongside KF-PLS. Both methods are benchmarked against non-linear regression techniques using two hyperspectral remote sensing datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
regressionRetrievalSimilar Papers 제목 키워드 기반
Kernel Extreme Learning Machine Optimized by the Sparrow Search Algorithm for Hyperspectral Image Classification
To improve the classification performance and generalization ability of the hyperspectral image classification algorithm, this paper uses Multi-Scale Total Variation (MSTV) to extract the spectral features, local binary …
ClassificationHyperspectral Image Classificationimage-classificationImage ClassificationHKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search
Recent neural architecture search (NAS) based approaches have made great progress in hyperspectral image (HSI) classification tasks. However, the architectures are usually optimized independently of the network weights, …
Neural Architecture SearchCombining multiscale features for classification of hyperspectral images: a sequence based kernel approach
Nowadays, hyperspectral image classification widely copes with spatial information to improve accuracy. One of the most popular way to integrate such information is to extract hierarchical features from a multiscale segm…
Classification Of Hyperspectral ImagesGeneral ClassificationHyperspectral Image Classificationimage-classification+1Randomized kernels for large scale Earth observation applications
Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has…
ClassificationEarth ObservationGeneral Classificationimage-classification+4InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments
Classic designs of hyperspectral instrumentation densely sample the spatial and spectral information of the scene of interest. Data may be compressed after the acquisition. In this paper we introduce a framework for the …