Multi-class Object Counting Network with Adaptive Class Alignment Loss for Remote Sensing Images

Accurately estimating the number of objects across different categories is crucial in remote sensing images for applications ranging from urban planning and environmental monitoring to disaster management. Although there are a few studies about multi-class object counting for remote sensing images, these methods may suffer from the issues of class imbalance, inter-class interference, or inability to capture fine-grained features. Thus, we propose a novel network called Multi-class Object Counting Network with Adaptive Class Alignment loss (ACA-MOCN) for multi-class object counting in remote sensing images. ACA-MOCN newly designs a Adaptive Class Alignment (ACA) loss function that can effectively balance the losses across multi-class objects and reduce interference between classes. In addition, ACA-MOCN also designs a Filtering Feature Pyramid Network (FFPN) structure to facilitate an effective feature fusion mechanism, enabling the integration of granular and detailed information. Experimental results on the two public datasets demonstrate that ACA-MOCN is superior when handling large scale multi-class object counting tasks in remote sensing images.

Recommended citation: Z. Zhu, L. Zhang, H. -Y. Ma, X. -Y. Wei and Y. Zhang, "Multi-class Object Counting Network with Adaptive Class Alignment Loss for Remote Sensing Images. IEEE Signal Processing Letters.
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