Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology

scholarly journals Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology

2019 ◽  
Vol 8 (4) ◽  
pp. 181 ◽  
Author(s):  
Xueyuan Zhu ◽  
Ying Li ◽  
Qiang Zhang ◽  
Bingxin Liu
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Film Thickness ◽  
Oil Spill ◽  
Spectral Characteristics ◽  
Hyperspectral Remote Sensing ◽  
Support Vector ◽  
Oil Film ◽  
Remote Sensing Technology ◽  
Sensing Technology

Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified.

A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction

2017 ◽  
Vol 31 (04) ◽  
pp. 1752001 ◽  
Author(s):  
Jing Zhang ◽  
Qianlan Zhou ◽  
Li Zhuo ◽  
Wenhao Geng ◽  
Suyu Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Superior Performance ◽  
Endmember Extraction ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Hyperspectral Remote Sensing Images ◽  
Hyperspectral Remote Sensing Image

With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.

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