Pooling layers (e.g., max and average) may overlook important information
encoded in the spatial arrangement of pixel intensity and/or feature values. We
propose a novel lacunarity pooling layer that aims to capture the spatial
heterogeneity of the feature maps by evaluating the variability within local
windows. The layer operates at multiple scales, allowing the network to
adaptively learn hierarchical features. The lacunarity pooling layer can be
seamlessly integrated into any artificial neural network architecture.
Experimental results demonstrate the layer's effectiveness in capturing
intricate spatial patterns, leading to improved feature extraction
capabilities. The proposed approach holds promise in various domains,
especially in agricultural image analysis tasks. This work contributes to the
evolving landscape of artificial neural network architectures by introducing a
novel pooling layer that enriches the representation of spatial features. Our
code is publicly available.

通过引入新型的空洞汇聚层来丰富空间特征的表示，该方法在多个尺度上操作，能够自适应地学习分层特征，实验证明其在捕捉复杂空间模式和改进特征提取能力方面的有效性。