A Lightweight Network for Water Body Segmentation in Agricultural Remote Sensing Using Learnable Kalman Filters and Attention Mechanisms
Source abstract
Abstract
Precise identification of water bodies in agricultural watersheds is crucial for irrigation, water resource management, and flood disaster prevention. However, the spectral noise caused by complex light and shadow interference and water quality differences, combined with the diverse shapes of water bodies and the high computational cost of image processing, severely limits the accuracy of water body recognition in agricultural watersheds. This paper proposed a lightweight and efficient learnable Kalman filter and Deformable Convolutional Attention Network (LKF-DCANet). The encoder is built using a shallow Channel Attention-Enhanced Deformable Convolution module (CADCN), while the decoder combines a Convolutional Additive Token Mixer (CATM) and a learnable Kalman filter (LKF) to achieve adaptive noise suppression and enhance global context modeling. Additionally, a feature-based knowledge distillation strategy is employed to further improve the representational capacity of the lightweight model. Experimental results show that LKF-DCANet achieves an Intersection over Union (IoU) of 85.95% with only 0.22 M parameters on a public dataset. When transferred to a self-constructed UAV dataset, it achieves an IoU of 96.28%, demonstrating strong generalization ability. All experiments are conducted on RGB optical imagery, confirming that LKF-DCANet offers an efficient and highly versatile solution for water body segmentation in precision agriculture.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
Key Laboratory of Investigation, Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources, Chengdu 610045, China
College of Resources, Sichuan Agricultural University, Chengdu 611130, China
Funding
Open Fund of Key Laboratory of Investigation, Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources · Observation and Research Station of Land Ecology and Land Use in Chengdu Plain, Ministry of Natural Resources, P.R. China · Natural Science Foundation of Sichuan, China · Provincial Undergraduate Training Program on Innovation and Entrepreneurship
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