StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction

Advancements in deep learning are revolutionizing the classification of remote-sensing images. Transformer-based architectures, utilizing self-attention mechanisms, have emerged as alternatives to conventional convolution methods, enabling the capture of long-range dependencies along with global relationships in the image. Motivated by these advancements, this paper presents StrideNET, a novel dual-branch architecture designed for terrain recognition and implicit properties estimation. The terrain recognition branch utilizes the Swin Transformer, leveraging its hierarchical representation and low computational cost to efficiently capture both local and global features. The terrain properties branch focuses on the extraction of surface properties such as roughness and slipperiness using a statistical texture analysis method. By computing surface terrain properties, an enhanced environmental perception can be obtained. The StrideNET model is trained on a dataset comprising four target terrain classes: Grassy, Marshy, Sandy, and Rocky. StrideNET attains competitive performance compared to contemporary methods. The implications of this work extend to various applications, including environmental monitoring, land use and land cover (LULC) classification, disaster response, precision agriculture, and much more.