Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
top in numerous areas, namely computer vision (CV), speech recognition, and natural …
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 …
[图书][B] Digital signal processing with Kernel methods
A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …
processing algorithms, with application to communications, multimedia, and biomedical …
Unsupervised change detection in multispectral remotely sensed imagery with level set methods
Y Bazi, F Melgani, HD Al-Sharari - IEEE Transactions on …, 2010 - ieeexplore.ieee.org
In this paper, the unsupervised change-detection problem in remote sensing images is
formulated as a segmentation issue where the discrimination between changed and …
formulated as a segmentation issue where the discrimination between changed and …
Unsupervised change detection with expectation-maximization-based level set
The level set method, because of its implicit handling of topological changes and low
sensitivity to noise, is one of the most effective unsupervised change detection techniques …
sensitivity to noise, is one of the most effective unsupervised change detection techniques …
Hyperspectral remote sensing of urban areas
P Hardin, A Hardin - Geography Compass, 2013 - Wiley Online Library
The use of airborne hyperspectral sensors for urban analysis represents a significant
advance in remote sensing. The greatest challenges to effectively using urban hyperspectral …
advance in remote sensing. The greatest challenges to effectively using urban hyperspectral …
A new dimensionality reduction algorithm for hyperspectral image using evolutionary strategy
Reducing the redundancy of spectral information is an important technique in classification
of hyperspectral image. The existing methods are classified into two categories: feature …
of hyperspectral image. The existing methods are classified into two categories: feature …
Multigranularity multiclass-layer Markov random field model for semantic segmentation of remote sensing images
C Zheng, Y Zhang, L Wang - IEEE Transactions on Geoscience …, 2020 - ieeexplore.ieee.org
Semantic segmentation is one of the most important tasks in remote sensing. However, as
spatial resolution increases, distinguishing the homogeneity of each land class and the …
spatial resolution increases, distinguishing the homogeneity of each land class and the …
Semantic segmentation of remote sensing imagery using an object-based Markov random field model with auxiliary label fields
The Markov random field (MRF) model has attracted great attention in the field of image
segmentation. However, most MRF-based methods fail to resolve segmentation …
segmentation. However, most MRF-based methods fail to resolve segmentation …
Semantic segmentation of remote sensing imagery using object-based Markov random field model with regional penalties
C Zheng, L Wang - IEEE Journal of Selected Topics in Applied …, 2014 - ieeexplore.ieee.org
This paper proposes a novel object-based Markov random field model (OMRF) for semantic
segmentation of remote sensing images. First, the method employs the region size and edge …
segmentation of remote sensing images. First, the method employs the region size and edge …