基于深度特征融合的空间植物图像分割算法

空间植物培养实验作为空间科学的一项重要研究，通常会获得大量的植物序列图像，传统的处理方法多采用人工观察，以供后续的进一步分析。本文提出一种基于多尺度深度特征融合的空间植物分割算法。该方法应用全卷积深度神经网络来提取多尺度特征，并分层次地融合由深层到浅层的特征，以达到对植物进行像素级的识别。分层次的特征融合了语义信息、中间层信息和几何特征，提高了分割的准确性。实验表明该方法在分割准确性方面表现良好，能够自动提取空间植物实验中的有效信息。

Space Plant Image Segmentation Based on Deep Features Fusion

As a key research in space science, space plant experiment usually obtains massive plant sequence images. The traditional processing methods are mostly observed manually for further analysis. This paper proposes a space plant image segmentation algorithm based on multi-scale deep feature fusion. This method uses a full-convolution deep neural network to extract multi-scale features, and hierarchically fuses features from deep to shallow to achieve pixel-level segmentation of plants. The hierarchical features fuse semantic information, middle layer information, and geometric features to improve segmentation accuracy. Experiments demonstrate that the method performs well in segmentation accuracy and can automatically extract useful information in space plant experiments.