Marine Boat Lights Detection Method Based on Sparse Representation for Low Light Remote Sensing Images During Night
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摘要: 针对目前基于低照度遥感影像对夜间海上船舶检测存在的目标特征挖掘不足的问题，该文设计了一种可使船舶目标样本和背景噪声样本最小错分的稀疏度指标，提出一种基于稀疏编码算法和字典学习算法的低照度遥感影像夜间船舶灯光检测算法，并将其应用于墨西哥湾北部海域、天津港南侧海域和上海港东侧海域，检测精确度分别为96.36%, 95.12%, 86.26%，召回率分别为96.36%, 92.86%, 94.19%，调和平均值分别为96.36%, 93.98%, 90.05%；进一步地，该文将此算法与3种典型低照度遥感影像夜间海上船舶检测算法进行了对比分析，结果表明该文算法更具有优越性能，可为夜间海上船舶的检测提供新的思路。Abstract: To address the problem of insufficient target feature mining in nighttime marine boat detection based on low light remote sensing images, a new sparsity index is designed to minimize the misclassification of boat lights samples and background noises samples, and a detection algorithm for boat lights based on sparse coding and dictionary learning is proposed in this paper. The proposed algorithm is applied to the northern sea area of the Gulf of Mexico, the sea area south of Tianjin Port, and the sea area east of Shanghai Port, and the detection precision is 96.36%, 95.12%, 86.26%, recall rate is 96.36%, 92.86%, 94.19%, and the harmonic mean is 96.36%, 93.98%, 90.05% respectively. Furthermore, the proposed algorithm is compared with three typical marine boat lights detection method for low light remote sensing images during night, demonstrating that the proposed algorithm has a superior performance and provides a new idea for marine boat detection during night.
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Key words:
- Low light remote sensing /
- Target detection /
- Boat detection /
- Sparse representation
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表 1 常用的稀疏度指标及其计算公式
稀疏度指标 计算公式 $ {l_p} $范数 $ {l_p} = {\left( {\displaystyle\sum\limits_{i = 1}^N {{{\left| {{x_i}} \right|}^p}} } \right)^{{1 \mathord{\left/ {\vphantom {1 p}} \right. } p}}},0 < p \le 1 $ (4) $ {l_2} $范数与$ {l_1} $范数之比 $ {{{l_2}} \mathord{\left/ {\vphantom {{{l_2}} {{l_1}}}} \right. } {{l_1}}} = {{\sqrt {\displaystyle\sum\limits_{i = 1}^N {x_i^2} } } \mathord{\left/ {\vphantom {{\sqrt {\displaystyle\sum\limits_{i = 1}^N {x_i^2} } } {\displaystyle\sum\limits_{i = 1}^N {\left| {{x_i}} \right|} }}} \right. } {\displaystyle\sum\limits_{i = 1}^N {\left| {{x_i}} \right|} }} $ (5) Hoyer系数 ${\rm{Hoyer} } = { {\left( {\sqrt N - \dfrac{ { {l_1} } }{ { {l_2} } } } \right)} \mathord{\left/ {\vphantom { {\left( {\sqrt N - \frac{ { {l_1} } }{ { {l_2} } } } \right)} {\left( {\sqrt N - 1} \right)} } } \right. } {\left( {\sqrt N - 1} \right)} }$ (6) Gini系数 ${\rm{Gini} } = 1 - \dfrac{2}{ { {l_1} } }\displaystyle\sum\limits_{i = 1}^N { {x_i} } \frac{ {N - i + 1/2} }{N},|{x_1}| \le |{x_2}| \le \cdots \le \left| { {x_N} } \right|$ (7) 
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表 2 各稀疏度指标对船舶灯光和背景噪声的区分度
稀疏度指标 $ {\rho _{{\text{target}}}} $ ${\rho _{ {{\rm{noise}}} } }$ $ {J_{\min }} $ $ {l_0} $范数 1.00 1.00 48.00 $ {l_1} $范数 0.39 0.66 32.43 $ {l_{{1 \mathord{\left/ {\vphantom {1 2}} \right. } 2}}} $范数 0.34 0.43 12.01 $ {{{l_2}} \mathord{\left/ {\vphantom {{{l_2}} {{l_1}}}} \right. } {{l_1}}} $ 0.40 0.80 2.76 Hoyer系数 0.40 0.80 7.72 Gini系数 0.98 1.00 4.57
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表 3 本文算法在3个不同海域的检测性能评价指标值
研究区域 $ P $ $ R $ ${\rm{F} }1$ 墨西哥湾北部海域 0.9636 0.9636 0.9636 天津港南侧海域 0.9512 0.9286 0.9398 上海港东侧海域 0.8626 0.9419 0.9005
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表 4 已有的低照度遥感影像夜间海上船舶灯光检测算法和本文算法在验证集和测试集上的检测性能评价指标值
算法 验证集 测试集 $ P $ $ R $ ${\rm{F}}1$ $ P $ $ R $ $ {\rm{F}}1 $ 基于SMI特征的检测算法 0.9327 0.9000 0.9161 0.8951 0.9026 0.8988 基于辐亮度梯度特征的检测算法 0.9342 0.9150 0.9245 0.9048 0.9013 0.9030 CFAR检测算法 0.9119 0.8750 0.8931 0.8699 0.8764 0.8731 本文算法 0.9585 0.9750 0.9667 0.9731 0.9476 0.9602
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表 5 已有的低照度遥感影像夜间海上船舶灯光检测算法在3个不同海域的检测性能评价指标值
算法 墨西哥湾北部海域 天津港南侧海域 上海港东侧海域 $ P $ $ R $ $ {\rm{F}}1 $ $ P $ $ R $ $ {\rm{F}}1 $ $ P $ $ R $ $ {\rm{F}}1 $ 基于SMI特征的检测算法 0.8364 0.9200 0.8762 0.9250 0.8810 0.9025 0.8659 0.8256 0.8453 基于辐亮度梯度特征的检测算法 0.7391 0.6800 0.7083 0.7209 0.7381 0.7294 0.7955 0.8140 0.8046 CFAR检测算法 0.7727 0.6800 0.7234 0.8095 0.8095 0.8095 0.8256 0.8256 0.8256
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