Results 81 to 90 of about 345,137 (201)

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

Remote Sensing, 2018
When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery ...
Cong Wang, Lei Zhang, Wei Wei, Yanning Zhang   +3 more
doaj   +1 more source

Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing [PDF]

, 2014
As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral ...
Han, Junwei, Zabalza, Jaime, Yang, Mingqiang, Zhang, Yi, Ren, Jinchang, Marshall, Stephen, Wang, Jun   +6 more
core   +2 more sources

Precise Crop Classification Using Spectral-Spatial-Location Fusion Based on Conditional Random Fields for UAV-Borne Hyperspectral Remote Sensing Imagery

Remote Sensing, 2019
The precise classification of crop types is an important basis of agricultural monitoring and crop protection. With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-borne hyperspectral remote sensing imagery with high spatial ...
Lifei Wei, Ming Yu, Yajing Liang, Ziran Yuan, Can Huang, Rong Li, Yiwei Yu   +6 more
doaj   +1 more source

Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]

, 2013
Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Eches, Olivier, Benediktsson, Jon Atli, Dobigeon, Nicolas, Tourneret, Jean-Yves   +3 more
core   +1 more source

Hyperspectral imagery super-resolution by sparse representation and spectral regularization

EURASIP Journal on Advances in Signal Processing, 2011
For the instrument limitation and imperfect imaging optics, it is difficult to acquire high spatial resolution hyperspectral imagery. Low spatial resolution will result in a lot of mixed pixels and greatly degrade the detection and recognition ...
Zhao Yongqiang, Yang Jinxiang, Zhang Qingyong, Song Lin, Cheng Yongmei, Pan Quan   +5 more
doaj  

Explorable 3D Hyperspectral Models from Multi-Angle Gimballed LWIR Pushbroom Imagery

Remote Sensing
Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition ...
Nikolay Golosov, Guido Cervone, Mark Salvador   +2 more
doaj   +1 more source

A Novel Fully Convolutional Auto-Encoder Based on Dual Clustering and Latent Feature Adversarial Consistency for Hyperspectral Anomaly Detection

Remote Sensing
With the development of artificial intelligence, the ability to capture the background characteristics of hyperspectral imagery (HSI) has improved, showing promising performance in hyperspectral anomaly detection (HAD) tasks.
Rui Zhao, Zhiwei Yang, Xiangchao Meng, Feng Shao   +3 more
doaj   +1 more source

A Review of Virtual Dimensionality for Hyperspectral Imagery

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018
Virtual dimensionality (VD) is originally defined as the number of spectrally distinct signatures in hyperspectral data. Unfortunately, there is no provided specific definition of what “spectrally distinct signatures” are. As a result, many techniques developed to estimate VD have produced various values for VD with different interpretations.
openaire   +2 more sources

Models and Methods for Automated Background Density Estimation in Hyperspectral Anomaly Detection [PDF]

, 2012
Detecting targets with unknown spectral signatures in hyperspectral imagery has been proven to be a topic of great interest in several applications. Because no knowledge about the targets of interest is assumed, this task is performed by searching the ...
VERACINI, TIZIANA
core  

Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery. [PDF]

Sensors (Basel), 2023
Lian X, Zhao E, Zheng W, Peng X, Li A, Zhen Z, Wen Y.   +6 more
europepmc   +1 more source