Paper Info
Title
Accurate Ulva prolifera regions extraction of UAV images with superpixel and CNNs for ocean environment monitoring.
Abstract
UAV (Unmanned Aerial Vehicle) monitoring mounted with high resolution camera is a rising way to monitor the ocean environment, and it can make up the shortages of low spatial and temporal resolutions of SAR images. How to get the accurate regions of Ulva prolifera in the very high-resolution images remains a lot of challenges. Due to the limitation of GPU memory, the popular pixel-level image segmentation methods cannot deal with the raw resolution images(Up to 6000*4000). In this paper, we propose a novel framework to get the Ulva prolifera regions, which incorporates both superpixel segmentation and CNN classification and can deal with raw resolution images. We first process the raw images with superpixel algorithm to generate local multi-scale patches. And then a binary classification CNN model can be trained with the labeled patches. With the result of superpixel segmentation and the classification of CNN model, a more detailed segmentation of Ulva prolifera can be obtained. Two datasets UlvaDB-1 and UlvaDB-2 are also proposed in this paper. The experiment results show that the proposed method can achieve state-of-the-art performance compared with the recent pixel-level segmentation and instance-aware semantic segmentation methods.
Year
DOI
Venue
2019
10.1016/j.neucom.2018.06.088
Neurocomputing
Keywords
Field
DocType
Superpixel,CNN,Pixel-level segmentation,Ulva prolifera
Binary classification,Pattern recognition,Segmentation,Ocean environment,Image segmentation,Ulva prolifera,Artificial intelligence,Economic shortage,Mathematics,Superpixel segmentation
Journal
Volume
ISSN
Citations 
348
0925-2312
0
PageRank 
References 
Authors
0.34
43
7
Name
Order
Citations
PageRank
Shengke Wang143.13
Lu Liu21501170.70
Liang Qu300.68
Changyin Yu400.68
Yujuan Sun5262.37
Feng Gao621.39
Junyu Dong79923.43