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

  • role： First author第一作者
  • Affiliation：

    Information Engineering University, Institute of Geospatial Information, Zhengzhou 450001, China

  • Email：xjf4606@foxmail.com
  • Introduction：徐俊峰，1990生,男,博士研究生, 研究方向为遥感影像变化检测的理论和应用。E-mail: xjf4606@foxmail.com
XU Junfeng1,  
  • Affiliation：

    Information Engineering University, Institute of Geospatial Information, Zhengzhou 450001, China

ZHANG Baoming1,  
  • Affiliation：

    Information Engineering University, Institute of Geospatial Information, Zhengzhou 450001, China

YU Donghang1,  
  • Affiliation：

    Information Engineering University, Institute of Geospatial Information, Zhengzhou 450001, China

LIN Yuzhun1,  
  • Affiliation：

    Information Engineering University, Institute of Geospatial Information, Zhengzhou 450001, China

GUO Haitao1

résumé

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.

mots-clés

remote sensing;change detection;multi-feature fusion;aircraft target;high-resolution remote sensing images;IR-MAD;convolutional neural network

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