Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 573-577, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-573-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 573-577, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-573-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

RESEARCH ON HIGH ACCURACY DETECTION OF RED TIDE HYPERSPECRRAL BASED ON DEEP LEARNING CNN

Y. Hu1,2, Y. Ma2, and J. An1 Y. Hu et al.
  • 1Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
  • 2The First Institute of Oceanography, SOA, Qingdao 266061, China

Keywords: Red Tide, CNN, Hyperspectral, Remote Sensing, Glint

Abstract. Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.