Remote-Sensing Interpretation for Soil Elements Using Adaptive Feature Fusion Network

Abstract

Soil elements refer to different types of soil with unique colors, textures, and particle sizes. Their interpretation is essential for agriculture, ecological environment and land permeability assessment. This typically requires experts with dual knowledge in geology and remote sensing. With the increasing volume of remote sensing data, the traditional “visual interpretation" and “field survey" technique is no longer sufficient to meet the demands. Because of the challenges such as the fine structure of soil, complex and variable natural scenes, and strong spatial variability, there remains a considerable gap between the accuracy of deep learning-based methods and expert interpretation. To improve the accuracy of intelligent soil elements interpretation, this study proposes a soil interpretation framework coupling implicit knowledge with multispectral image (SIFCIM). This framework quantifies implicit knowledge, such as interpretation symbol and terrain feature, into matrix data (interpretation symbol distance field and digital elevation model). To align with the SIFCIM, an Implicit-Knowledge-Guided Adaptive Feature Fusion Network (IAFFNet) is constructed, which enhances the utilization efficiency of auxiliary features through an adaptive implicit feature fusion module and a global feature dependence module. Experimental results demonstrate that IAFFNet outperforms interpretation methods with single remote sensing image, achieving approximately 4.34% and 6.62% improvements in overall pixel accuracy and mean intersection over union, respectively. These results validate the effectiveness and robustness of the implicit-knowledge-guided approach in soil elements interpretation. To our knowledge, this work is the first to apply the concept of implicit knowledge to soil elements interpretation, providing a novel insight for related research.

Topics & Concepts