计算机科学
合成孔径雷达
人工智能
遥感
算法
变更检测
图像分辨率
模式识别(心理学)
卷积神经网络
计算机视觉
像素
雷达成像
恒虚警率
深度学习
高分辨率
图像(数学)
散斑噪声
作者
João Gabriel Vinholi,Danilo Silva,Renato Machado,Mats I. Pettersson
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2020-10-08
卷期号:: 1-5
标识
DOI:10.1109/lgrs.2020.3027382
摘要
This letter presents an incoherent change detection algorithm (CDA) for wavelength-resolution synthetic aperture radar (SAR) based on convolutional neural networks (CNNs). The proposed CDA includes a segmentation CNN, which localizes potential changes, and a classification CNN, which further analyzes these candidates to classify them as real changes or false alarms. Compared to state-of-the-art solutions on the CARABAS-II data set, the proposed CDA shows a significant improvement in performance, achieving, in a particular setting, a detection probability of 99% at a false alarm rate of 0.0833/km².
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