【摘要】 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。并选取黑龙江省海伦市全域作为研究区以验证本文模型。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；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。综上，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。更多还原

【Abstract】 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 considerably affected by soil erosion, with erosion gullies representing a prominent manifestation of this issue. Erosion gullies, which are formed because of soil erosion, often interconnect within a hydrological network, creating a tree-like distribution of erosion gully systems that inflict severe damage on cultivated land. Therefore, accurate identification and detection of erosion gullies are pivotal for safeguarding arable land.This study explores the feasibility of utilizing remote sensing imagery for erosion gully detection and identification, taking advantage of its vast coverage and multiple capture instances. We introduce a novel deep learning model based on a multiscale dense dilated convolutional neural network tailored for erosion gully recognition. Our model incorporates dense connections of multiscale dilated convolutional residual modules and is optimized to aggregate the multilevel spatial features of erosion gullies.The research is conducted in Hailun City, Heilongjiang Province, which serves as the study area. Our approach involves cropping remote sensing images into predefined patches, which are then annotated to construct training datasets comprising two categories: erosion gullies and non-gullies. Subsequently, the model is trained on the training dataset and evaluated on the test dataset, with weight selection being based on the highest test dataset accuracy. Utilizing the selected weights, we perform sliding window identification across the entire Hailun City area, thereby generating spatial distribution data for erosion gullies. Furthermore, we realize erosion gully area localization on the basis of scene-level labels and class activation maps to offer guidance for boundary extraction.The findings demonstrate the efficacy of the proposed model, which achieves an impressive overall accuracy of 95.80% and a kappa coefficient of 0.9152. It outperforms traditional deep learning models, such as GoogLeNet, ResNet, DenseNet, and Swin-Transformer.Notably, the overall accuracy in the sliding window recognition phase decreases slightly compared with that in the test phase because of the increased complexity of remote sensing imagery in practical applications. To address this challenge, we recommend using a fusion of remote satellite images and street view imagery in future research to enhance recognition capabilities in complex scenarios.This study underscores the effectiveness of erosion gully identification through the application of a multiscale dense dilated convolutional neural network. It serves provides precise spatial distribution data concerning erosion gullies, thereby contributing to integrated land management in the black soil region of Northeast China.更多还原