Using deep learning in image hyper spectral segmentation, classification, and detection

Xiuying Zhao; Zhenyu Su

doi:10.1117/12.2307376

20 February 2018 Using deep learning in image hyper spectral segmentation, classification, and detection

Xiuying Zhao, Zhenyu Su

Proceedings Volume 10697, Fourth Seminar on Novel Optoelectronic Detection Technology and Application; 106972U (2018) https://doi.org/10.1117/12.2307376
Event: Fourth Seminar on Novel Optoelectronic Detection Technology and Application, 2017, Nanjing, China

Abstract

Recent years have shown that deep learning neural networks are a valuable tool in the field of computer vision. Deep learning method can be used in applications like remote sensing such as Land cover Classification, Detection of Vehicle in Satellite Images, Hyper spectral Image classification. This paper addresses the use of the deep learning artificial neural network in Satellite image segmentation. Image segmentation plays an important role in image processing. The hue of the remote sensing image often has a large hue difference, which will result in the poor display of the images in the VR environment. Image segmentation is a pre processing technique applied to the original images and splits the image into many parts which have different hue to unify the color. Several computational models based on supervised, unsupervised, parametric, probabilistic region based image segmentation techniques have been proposed. Recently, one of the machine learning technique known as, deep learning with convolution neural network has been widely used for development of efficient and automatic image segmentation models. In this paper, we focus on study of deep neural convolution network and its variants for automatic image segmentation rather than traditional image segmentation strategies.

Citation Download Citation

Xiuying Zhao and Zhenyu Su "Using deep learning in image hyper spectral segmentation, classification, and detection", Proc. SPIE 10697, Fourth Seminar on Novel Optoelectronic Detection Technology and Application, 106972U (20 February 2018); https://doi.org/10.1117/12.2307376

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available