An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges
Hyperspectral images (HSIs) have a cube form containing spatial information in two
dimensions and rich spectral information in the third one. The high volume of spectral bands …
dimensions and rich spectral information in the third one. The high volume of spectral bands …
Discriminant analysis-based dimension reduction for hyperspectral image classification: A survey of the most recent advances and an experimental comparison of …
Hyperspectral imagery contains hundreds of contiguous bands with a wealth of spectral
signatures, making it possible to distinguish materials through subtle spectral discrepancies …
signatures, making it possible to distinguish materials through subtle spectral discrepancies …
Folded LDA: extending the linear discriminant analysis algorithm for feature extraction and data reduction in hyperspectral remote sensing
The rich spectral information provided by hyperspectral imaging has made this technology
very useful in the classification of remotely sensed data. However, classification of …
very useful in the classification of remotely sensed data. However, classification of …
[BUCH][B] Multisensor data fusion and machine learning for environmental remote sensing
In the last few years the scientific community has realized that obtaining a better
understanding of interactions between natural systems and the man-made environment …
understanding of interactions between natural systems and the man-made environment …
Hyperspectral estimation of maize (Zea mays L.) yield loss under lodging stress
Q Sun, X Gu, L Chen, X Qu, S Zhang, J Zhou… - Field Crops Research, 2023 - Elsevier
The frequency and intensity of maize (Zea mays L.) yield disturbance caused by lodging
stress are getting higher and higher, so it is of great significance to take effective methods to …
stress are getting higher and higher, so it is of great significance to take effective methods to …
Segmentation-based linear discriminant analysis with information theoretic feature selection for hyperspectral image classification
The use of hyperspectral imaging sensors has greatly improved the classification of remotely
sensed data because of the abundant spectral information they offer. However, the …
sensed data because of the abundant spectral information they offer. However, the …
Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification
Y Bai, X Sun, Y Ji, W Fu, J Zhang - Multimedia Tools and Applications, 2024 - Springer
Deep learning models have been widely used in hyperspectral images classification.
However, the classification results are not satisfactory when the number of training samples …
However, the classification results are not satisfactory when the number of training samples …
Dimensionality reduction of hyperspectral images using pooling
A Paul, N Chaki - Pattern Recognition and Image Analysis, 2019 - Springer
Hyperspectral image having huge numbers of narrow and contiguous bands involves high
computation complexity in processing and analysing the image. Hence dimensionality …
computation complexity in processing and analysing the image. Hence dimensionality …
Binary coding based feature extraction in remote sensing high dimensional data
A binary coding based feature extraction (BCFE) method is proposed in this paper. In the
BCFE method, the spectral signature of each pixel of hyperspectral image is partitioned into …
BCFE method, the spectral signature of each pixel of hyperspectral image is partitioned into …
GLCM, Gabor, and morphology profiles fusion for hyperspectral image classification
A fusion method for combination of spectral and spatial features for classification
improvement of hyperspectral images is proposed in this paper. Gray level co-occurance …
improvement of hyperspectral images is proposed in this paper. Gray level co-occurance …