Efficient interpretation algorithm for foreign nautical chart substrate annotations considering semantics and image features

doi:10.11947/j.AGCS.2026.20260115

Abstract

Abstract:

In response to current issues in the automatic interpretation of foreign raster nautical chart substrate annotations, such as poor adaptability to complex scenarios, low efficiency, and the disconnection between semantics interpretation and image processing, this paper proposes an efficient interpretation algorithm for foreign nautical chart substrate annotations considering semantics and image features. Firstly, the algorithm constructs an image optimization strategy based on the visual representation features of substrate annotations. By applying land area masking and adaptive threshold segmentation, it effectively filters out interference from the complex background of raster charts. Subsequently, template matching technology is introduced to establish a substrate annotation template library based on Hu invariant moment features, achieving integrated interpretation for both character recognition and semantic parsing. Finally, to address the difficulty in constructing templates for composite annotations, a two-level annotation slice segmentation model considering semantic integrity is designed, solving the interpretation challenge of complex combined annotations. Experimental results show that: ①The proposed algorithm successfully achieves efficient interpretation of foreign chart substrate annotations, demonstrating high precision, recall, recognition completeness rate, and semantic accuracy during the processes of annotation recognition and semantic parsing.②Comparative experiments with existing methods under scenarios of varying complexity indicate that the proposed algorithm maintains higher recognition accuracy while further improving overall processing efficiency. Moreover, it effectively addresses issues of missed annotations and semantic parsing errors encountered by comparative methods in medium-and high-complexity scenarios, proving the superior robustness and efficiency of our approach.

Key words: nautical chart informatization, automatic extraction, substrate annotations, semantic parsing, template matching

CLC Number:

P285

Jian Dong, Chaoyu Tan, Lulu Tang, Zeyuan Dai, Tian Xie, Changlin Chen, Er Ni. Efficient interpretation algorithm for foreign nautical chart substrate annotations considering semantics and image features[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(9): 1567-1581.

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序号	Id号	原图底质	是否被识别	注记识别是否完整	语义信息提取是否精确
1	1	fS	是	是	精确提取：fine sand
2	2	fS. bkSh	是	是	精确提取：fine sand broken shells
3	3	S. G. bkSh	是	是	精确提取：sand gravel broken shells
4	4	S. bkSh	是	是	精确提取：sand broken shells
5	5	soM. fS. bkSh	是	是	精确提取：soft mud fine sand broken shells
⋮	⋮	⋮	⋮	⋮	⋮
35	35	M. S	是	否	语义缺失：sand
78	78	soM	是	否	语义缺失：soft
114	114	S. G. bkS	是	否	语义缺失：sand gravel broken sand

轮次	迭代数	起始学习率	学习率变化方式	批次大小
冻结轮次	50	10^-3	阶梯式衰减 lr=lr×α（α=0.9）不变化	4
解冻轮次	50	10^-4	阶梯式衰减 lr=lr×α（α=0.9）不变化	4

注记种类	查准率/（%）	查全率/（%）	语义提取准确率/（%）	处理时间/s
本文模型	100	100	100	5.95+0.28 （识别+语义解译）
简化模型A	100	100	100	5.95+19.65 （识别+语义解译）
简化模型B	100	91.01	100	8.32+0.28 （识别+语义解译）
文献[22]模型	100	91.01	100	8.32+22.38 （识别+语义解译）
现有模型（CRNN）	100	91.01	100	8.32+17.21 （识别+语义解译）

注记种类	查准率/（%）	查全率/（%）	语义提取准确率/（%）	处理时间/s
本文模型	100	91.01	100	6.11+0.25 （识别+语义解译）
简化模型A	100	91.01	100	6.11+20.87 （识别+语义解译）
简化模型B	100	81.82	88.89	9.06+0.25 （识别+语义解译）
文献[22]模型	100	81.82	88.89	9.06+24.26 （识别+语义解译）
现有模型（CRNN）	100	81.82	100	9.06+17.64 （识别+语义解译）