关键词: 卷积神经网络, 海陆分割, 语义分割, 遥感图像

Abstract:

Aiming at the problem of pixel classification error and boundary segmentation blur caused by the complex shape of land and dock, and the complex background in the sea-land semantic segmentation, this paper proposes a new method based on deep convolutional neural network for sea-land segmentation of remote sensing image. This method implements the pixel-by-pixel classification of the target in an end-to-end training manner.  Aiming at the problem of pixel classification error in sea-land segmentation, this paper designs three parallel coding structures with different scale images as input.  It can enrich the semantic information of feature representation and increase the pixel classification accuracy by merging the feature maps of different scales.  Aiming at the problem of boundary segmentation blurring in sea-land segmentation, this paper designs decoding structure which merges more fine position information in low coding layer structure to achieve a more accurate up-sample operation, restore dense location information, and improve the accuracy of sea-land segmentation.  At the same time, in order to effectively verify the network framework proposed in this paper, this paper constructs a new sea-land segmentation dataset HRSC2016-SL.  Compared with the latest semantic segmentation algorithms, the proposed algorithm achieves better segmentation results.

Key words: convolutional neural network, sea-land segmentation, semantic segmentation, remote sensing image