Hyperspectral remote sensing classifications: a perspective survey
Classification of hyperspectral remote sensing data is more challenging than multispectral
remote sensing data because of the enormous amount of information available in the many …
remote sensing data because of the enormous amount of information available in the many …
Convolutional neural network for the semantic segmentation of remote sensing images
M Alam, JF Wang, C Guangpei, LV Yunrong… - Mobile Networks and …, 2021 - Springer
In recent years, the success of deep learning in natural scene image processing boosted its
application in the analysis of remote sensing images. In this paper, we applied …
application in the analysis of remote sensing images. In this paper, we applied …
Gabor-filtering-based nearest regularized subspace for hyperspectral image classification
By coupling the nearest-subspace classification with a distance-weighted Tikhonov
regularization, nearest regularized subspace (NRS) was recently developed for …
regularization, nearest regularized subspace (NRS) was recently developed for …
Hyperspectral remote sensing image subpixel target detection based on supervised metric learning
The detection and identification of target pixels such as certain minerals and man-made
objects from hyperspectral remote sensing images is of great interest for both civilian and …
objects from hyperspectral remote sensing images is of great interest for both civilian and …
A big data clustering algorithm for mitigating the risk of customer churn
W Bi, M Cai, M Liu, G Li - IEEE Transactions on Industrial …, 2016 - ieeexplore.ieee.org
As market competition intensifies, customer churn management is increasingly becoming an
important means of competitive advantage for companies. However, when dealing with big …
important means of competitive advantage for companies. However, when dealing with big …
Training-and test-time data augmentation for hyperspectral image segmentation
Data augmentation helps improve generalization capabilities of deep neural networks when
only limited ground-truth training data are available. In this letter, we propose test-time …
only limited ground-truth training data are available. In this letter, we propose test-time …
Validating hyperspectral image segmentation
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing
community, and hence, automated approaches for precise segmentation of such imagery …
community, and hence, automated approaches for precise segmentation of such imagery …
A survey on image segmentation methods using clustering techniques
Image segmentation has been considered as the first step in the image processing. An
efficient segmentation result would make it easier for further analysis of image processing …
efficient segmentation result would make it easier for further analysis of image processing …
Unsupervised segmentation of hyperspectral images using 3-D convolutional autoencoders
Hyperspectral image analysis has become an important topic widely researched by the
remote sensing community. Classification and segmentation of such imagery help …
remote sensing community. Classification and segmentation of such imagery help …
Hyperspectral image segmentation using a new spectral unmixing-based binary partition tree representation
The binary partition tree (BPT) is a hierarchical region-based representation of an image in a
tree structure. The BPT allows users to explore the image at different segmentation scales …
tree structure. The BPT allows users to explore the image at different segmentation scales …