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.

StrideNET 是一种使用深度学习和 Transformer-based architectures 的双支路架构，用于地形识别和隐含属性估计，并通过统计纹理分析方法提取地表特性。

StrideNET：基于动态粗糙度提取的地形识别的 Swin Transformer

StrideNET: Swin Transformer for Terrain Recognition with Dynamic  Roughness Extraction

Automatic terrain recognition in Mars rover images is an important problem
not just for navigation, but for scientists interested in studying rock types,
and by extension, conditions of the ancient Martian paleoclimate and
habitability. Existing approaches to label Martian terrain either involve the
use of non-expert annotators producing taxonomies of limited granularity (e.g.
soil, sand, bedrock, float rock, etc.), or rely on generic class discovery
approaches that tend to produce perceptual classes such as rover parts and
landscape, which are irrelevant to geologic analysis. Expert-labeled datasets
containing granular geological/geomorphological terrain categories are rare or
inaccessible to public, and sometimes require the extraction of relevant
categorical information from complex annotations. In order to facilitate the
creation of a dataset with detailed terrain categories, we present a
self-supervised method that can cluster sedimentary textures in images captured
from the Mast camera onboard the Curiosity rover (Mars Science Laboratory). We
then present a qualitative analysis of these clusters and describe their
geologic significance via the creation of a set of granular terrain categories.
The precision and geologic validation of these automatically discovered
clusters suggest that our methods are promising for the rapid classification of
important geologic features and will therefore facilitate our long-term goal of
producing a large, granular, and publicly available dataset for Mars terrain
recognition.

该篇论文呈现了一种自主方法，用于在火星探测器图像中聚类沉积纹理，以便创建详细的地形类别数据集和地质分析，取得了较高的准确性和地质效验，为火星地形分类的快速识别提供了有前途的方法，并为实现生产大规模、细粒度和公开可用的火星地形识别数据集的长期目标开辟了道路。