Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Recent advances on spectral–spatial hyperspectral image classification: An overview and new guidelines
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in the
last four decades from being a sparse research tool into a commodity product available to a …
last four decades from being a sparse research tool into a commodity product available to a …
Multiple kernel learning for hyperspectral image classification: A review
With the rapid development of spectral imaging techniques, classification of hyperspectral
images (HSIs) has attracted great attention in various applications such as land survey and …
images (HSIs) has attracted great attention in various applications such as land survey and …
PCA-based edge-preserving features for hyperspectral image classification
Edge-preserving features (EPFs) obtained by the application of edge-preserving filters to
hyperspectral images (HSIs) have been found very effective in characterizing significant …
hyperspectral images (HSIs) have been found very effective in characterizing significant …
Salient band selection for hyperspectral image classification via manifold ranking
Saliency detection has been a hot topic in recent years, and many efforts have been devoted
in this area. Unfortunately, the results of saliency detection can hardly be utilized in general …
in this area. Unfortunately, the results of saliency detection can hardly be utilized in general …
Multiscale superpixelwise prophet model for noise-robust feature extraction in hyperspectral images
Despite various approaches proposed to smooth the hyperspectral images (HSIs) before
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
feature extraction, the efficacy is still affected by the noise, even using the corrected dataset …
Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging
Stacked autoencoders (SAEs), as part of the deep learning (DL) framework, have been
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …
recently proposed for feature extraction in hyperspectral remote sensing. With the help of …
Hyperspectral image classification using dictionary-based sparse representation
A new sparsity-based algorithm for the classification of hyperspectral imagery is proposed in
this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can …
this paper. The proposed algorithm relies on the observation that a hyperspectral pixel can …
Hyperspectral image classification via kernel sparse representation
In this paper, a novel nonlinear technique for hyperspectral image (HSI) classification is
proposed. Our approach relies on sparsely representing a test sample in terms of all of the …
proposed. Our approach relies on sparsely representing a test sample in terms of all of the …
Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features
Hyperspectral remote sensing imagery contains rich information on spectral and spatial
distributions of distinct surface materials. Owing to its numerous and continuous spectral …
distributions of distinct surface materials. Owing to its numerous and continuous spectral …
A short survey of hyperspectral remote sensing applications in agriculture
Hyperspectral sensors are devices that acquire images over hundreds of spectral bands,
thereby enabling the extraction of spectral signatures for objects or materials observed …
thereby enabling the extraction of spectral signatures for objects or materials observed …