Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion

Junfeng XU; Baoming ZHANG; Donghang YU; Yuzhun LIN; Haitao GUO

doi:10.11834/jrs.20208213

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Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion
Vol. 24, Issue 1, Pages: 37-52(2020)
Received：16 May 2018，

Published：07 January 2020
DOI： 10.11834/jrs.20208213
稿件说明：

移动端阅览

徐俊峰,张保明,余东行,林雨准,郭海涛.2020.多特征融合的高分辨率遥感影像飞机目标变化检测.遥感学报,24(1): 37-52 DOI： 10.11834/jrs.20208213.

XU Junfeng,ZHANG Baoming,YU Donghang,LIN Yuzhun,GUO Haitao. 2020. Aircraft target change detection for high-resolution remote sensing images using multi-feature fusion. Journal of Remote Sensing(Chinese). 24(1): 37-52 DOI： 10.11834/jrs.20208213.

摘要

为利用高分辨率遥感影像实现高精度的飞机目标变化检测，提出了一种自适应的多特征融合变化检测与深度学习相结合的方法。首先，通过加权迭代的多元变化检测法获取变化强度图，并结合自适应的直方图统计法自动获取显著的变化与不变化样本；然后，提取多时相影像的光谱、边缘和纹理特征，完成多特征融合的变化检测，并通过形态学处理得到变化图斑；最后，利用训练的NIN(Network in Network)结构的卷积神经网络飞机识别模型，完成变化图斑的类型判别，实现变化飞机的检测。实验结果表明，本文方法在两组数据的正确率分别达到100%和91.89%，均优于对比方法，能实现准确可靠的飞机目标变化检测。

Abstract

Multi-feature fusion has been widely employed for high-resolution remote sensing images change detection given its ability to reduce the influence of radiation difference

projection errors

and shadows. However

most multi-feature fusion methods depend on artificially designed fusion rules or man-made samples. Meanwhile

many methods for target change detection on bi-temporal images have successfully detected the changed areas yet fail to recognize the number and location of changed targets. To address these limitations

this paper proposes an aircraft target change detection method for high-resolution remote sensing images that combines adaptive multi-feature fusion for change detection with deep learning for target recognition.

First

Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) is applied to generate a change intensity map

and then adaptive histogram statistics are employed to calculate the threshold for the automatic acquisition of conspicuous changed and unchanged samples. Second

the edge and textural features are extracted respectively from multi-temporal images

and a multi-feature fusion change detection is carried out by using a support vector machine and the previous samples. The change polygons are obtained after morphological processing. Third

to prevent a false detection by a single threshold

a multi-threshold strategy is adopted

and raw images from the change polygons are recognized by using the aircraft recognition model that is trained by a few plane slices and the convolutional neural network with network in network.

The experiments for airport images change detection and final aircraft target change detection experiments are performed using two datasets. First

we compare our proposed method is compared with other thresholds and check whether to use multi-features should be used to highlight the effectiveness of our multi-feature fusion change detection method with adaptive samples. To verify its performance

we compare our method with some popular change detection methods are compared

including Multivariate Alteration Detection (MAD)

IR-MAD

principal component analysis

change vector analysis

robust change vector analysis

and iterative slow feature analysis. The experiments show that the overall accuracy of our proposed change detection method outperforms the other compared methods in terms of accuracy and false alarm rate. We obtain our target change detection results based on the change map

and validate the excellent performance of our proposed method based on its accuracy.

To fully use of the spectral

spatial

and textural features of high-resolution remote sensing images

we design an adaptive multi-feature fusion method for change detection that requires less manual work and reduces the influence of radiation difference

projection error

and shadows. We also propose an aircraft target change detection method by combining the multi-feature fusion change detection with target recognition using deep learning. The experimental results validate the excellent performance and reliability of our method.

关键词

Keywords

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