StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction

arXiv:2404.13270v1 Announce Type: cross
Abstract: 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 …

abstract architectures arxiv attention attention mechanisms classification convolution cs.cv cs.lg deep learning dependencies dynamic enabling extraction global image images paper recognition relationships remote-sensing self-attention sensing swin swin transformer transformer type