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冯权泷，江子航，牛博文，高秉博，杨建宇，杨柯.XXXX.东北黑土区侵蚀沟遥感识别的多尺度特征提取模型.遥感学报，XX（XX）： 1-11
FENG Quanlong，JIANG Zihang，NIU Bowen，GAO Bingbo，YANG Jiangyu，YANG Ke. XXXX. Multi-Scale Feature Extraction Model for Remote Sensing Identification of Erosion Gullies in Northeast China's Black Soil Region： A Case Study of Hailun City. National Remote Sensing Bulletin， XX（XX）：1-11
土壤侵蚀对东北黑土区的作物产量构成严重威胁，侵蚀沟是其主要表现之一。开展侵蚀沟识别和检测对东北黑土区土地综合治理等具有重要意义。本文基于多尺度稠密扩张卷积神经网络提出一个新的侵蚀沟遥感识别模型。该模型包括多个稠密连接的多尺度扩张卷积残差模块，可以更好地聚合侵蚀沟的多层级空间特征。选取黑龙江省海伦市全域作为研究区，试验结果表明本文模型取得了较好的识别效果，总体精度可达95.80%，kappa系数为0.9152，效果优于经典深度学习模型。同时，基于场景级标签和类激活图实现了对侵蚀沟区域的定位，可为侵蚀沟的边界提取提供参考。本文研究表明，基于多尺度稠密扩张卷积神经网络在东北黑土区开展侵蚀沟识别是有效的，可为东北黑土区土地综合治理提供精确的侵蚀沟空间分布数据。
Black soil is a valuable and essential soil resource
particularly in the northeastern region of China
where it serves as the primary grain-producing area. However
the quality of local agriculture is significantly impacted by soil erosion
with erosion gullies representing a prominent manifestation of this issue. Erosion gullies
formed due to soil erosion
often interconnect within a hydrological network
creating a tree-like distribution of erosion gully systems that inflict severe damage upon cultivated land. Therefore
the accurate identification and detection of erosion gullies are pivotal for safeguarding arable land.Objective This study explores the feasibility of utilizing remote sensing imagery for erosion gully detection and identification
leveraging its vast coverage and multiple capture instances. Additionally
we introduce a novel deep learning model tailored for erosion gully recognition based on a multi-scale dense dilated convolutional neural network. Our model incorporates dense connections of multi-scale dilated convolutional residual modules
optimized to aggregate the multi-level spatial features of erosion gullies.Method The research was conducted within Hailun City
located in Heilongjiang Province
serving as the study area. Our approach involved cropping remote sensing images into predefined patches
which were then annotated to construct training datasets comprising two categories: erosion gullies and non-gullies. Subsequently
the model was trained on the training dataset and evaluated on the test dataset
with weight selection based on the highest test dataset accuracy. Utilizing these selected weights
we performed sliding window identification across the entire Hailun City area
thereby generating spatial distribution data for erosion gullies. Furthermore
we accomplished erosion gully area localization based on scene-level labels and class activation maps
offering guidance for boundary extraction.Results Our findings demonstrate the efficacy of the proposed model
achieving an impressive overall accuracy of 95.80% and a kappa coefficient of 0.9152. This outperforms traditional deep learning models such as GoogLeNet
ResNet
DenseNet
and Swin-Transformer. Notably
during the sliding window recognition phase
the overall accuracy dipped slightly compared to the test phase due to the increased complexity of remote sensing imagery in practical applications. To address this challenge
we recommend incorporating a fusion of remote satellite images and street view imagery in future research to enhance recognition capabilities in complex scenarios.Conclusion This study underscores the effectiveness of erosion gully identification through the application of a multi-scale dense dilated convolutional neural network. It serves as a valuable tool for providing precise spatial distribution data concerning erosion gullies
thereby contributing to integrated land management in the black soil region of Northeast China.
侵蚀沟东北黑土区深度学习场景识别特征提取扩张卷积神经网络遥感监测耕地保护
Erosion gulliesBlack soil region in Northeast ChinaDeep learningFeature extractionScene recognitionDilated convolutional neural networkRemote sensing monitoringCropland protection
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