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Author:

Wu, Lifang (Wu, Lifang.) (Scholars:毋立芳) | He, Jiaoyu (He, Jiaoyu.) | Jian, Meng (Jian, Meng.) | Zhang, Jianan (Zhang, Jianan.) | Zou, Yunzhen (Zou, Yunzhen.)

Indexed by:

CPCI-S

Abstract:

Due to the various noises, the cloud image segmentation becomes a big challenge for atmosphere prediction. CNN is capable of learning discriminative features from complex data, but this may be quite time-consuming in pixel-level segmentation problems. In this paper we propose superpixel analysis based CNN (SP-CNN) for high efficient cloud image segmentation. SP-CNN employs image over-segmentation of superpixels as basic entities to preserve local consistency. SP-CNN takes the image patches centered at representative pixels in every superpixel as input, and all superpixels are classified as cloud or non-cloud part by voting of the representative pixels. It greatly reduces the computational burden on CNN learning. In order to avoid the ambiguity from superpixel boundaries, SP-CNN selects the representative pixels uniformly from the eroded superpixels. Experimental analysis demonstrates that SP-CNN guarantees both the effectiveness and efficiency in cloud segmentation.

Keyword:

Superpixel analysis Cloud image Cloud segmentation Convolutional neural networks

Author Community:

  • [ 1 ] [Wu, Lifang]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [He, Jiaoyu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Jian, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Zhang, Jianan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Zou, Yunzhen]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Jian, Meng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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Source :

2017 INTERNATIONAL CONFERENCE ON SYSTEMS, SIGNALS AND IMAGE PROCESSING (IWSSIP)

ISSN: 2157-8672

Year: 2017

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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