多层次的双流融合网络,用于在多源图像中对江进行分类.
Xuli Rao1,2, Chen Feng2, Jinshi Lin1
1Jinshan Soil and Water Conservation Research Center, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究引入了一种新的深度学习方法,用于使用无人机图像识别土壤侵蚀 (Benggangs). 该方法通过融合来自不同数据源的多尺度特征,准确地绘制了Benggangs的地图,从而改善了中国南部的土地管理.
科学领域:
- 地质科学 地质科学
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
背景情况:
- 在中国南部的山区,干 (土壤侵蚀) 普遍存在,使土地管理和生态工作复杂化.
- 准确地识别和绘制Benggangs对于有效的控制策略至关重要.
- 深度学习为Benggang分类提供了先进的功能,但从多源图像中提取和融合特征仍然存在挑战.
研究的目的:
- 开发和评估一种使用多尺度特征和双流聚变网络 (MS-TSFN) 的新的Benggang分类方法.
- 为了应对选择合适的特征提取和融合技术的挑战,用于Benggang识别中的多源图像数据.
主要方法:
- 从无人机获取的数字正方形图 (DOM) 和数字表面模型 (DSM) 数据中提取了关键的地形特征 (斜率,侧面,曲率,山坡阴影,边缘).
- 采用双流融合网络与ResNeSt骨干,从多源图像中提取多尺度特征.
- 使用基于注意力的特征融合块进行深度信息整合,并使用决策融合块进行最终分类.
主要成果:
- 与现有方法相比,拟议的MS-TSFN方法在提取Benggangs的空间特征和纹理方面表现出优异的性能.
- 使用DOM,Canny边缘检测和DSM特征的组合实现了最佳结果.
- 该模型获得了高精度 (92.76%),精度 (85.00%),回忆 (77.27%) 和F1得分 (0.8059).
结论:
- 该MS-TSFN方法提供了一个有效的解决方案Benggang分类,特别是在复杂的地形.
- 来自DOM和DSM数据的多尺度特征的融合显著提高了识别准确性.
- 这种方法为风易发地区的生态保护和土地管理提供了有前途的工具.
相关概念视频
Classification of Signals
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Systems-I
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Aggregates Classification
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Uniform Depth Channel Flow
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
Rapidly Varying Flow
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...


