山崩
联营
计算机科学
卷积神经网络
人工智能
磨坊
频道(广播)
比例(比率)
深度学习
特征提取
模式识别(心理学)
遥感
地质学
工程类
地图学
地理
岩土工程
电信
机械工程
作者
TangXiaochuan,LiuMingzhe,ZhongHao,JuYuanzhen,LiWeile,XuQiang
摘要
Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.
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