Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM)

Te Han,Zefeng Lin,Bin Zou,Xin Yang,Yuqi Tang,Huihui Feng

doi:10.3390/rs13234918

Te Han, Zefeng Lin + Show 4 more

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https://doi.org/10.3390/rs13234918

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Abstract

To solve the problems of susceptibility to image noise, subjectivity of training sample selection, and inefficiency of state-of-the-art change detection methods with heterogeneous images, this study proposes a post-classification change detection method for heterogeneous images with improved training of hierarchical extreme learning machine (HELM). After smoothing the images to suppress noise, a sample selection method is defined to train the HELM for each image, in which the feature extraction is respectively implemented for heterogeneous images and the parameters need not be fine-tuned. Then, the multi-temporal feature maps extracted from the trained HELM are segmented to obtain classification maps and then compared to generate a change map with changed types. The proposed method is validated experimentally by using one set of synthetic aperture radar (SAR) images obtained from Sentinel-1, one set of optical images acquired from Google Earth, and two sets of heterogeneous SAR and optical images. The results show that compared to state-of-the-art change detection methods, the proposed method can improve the accuracy of change detection by more than 8% in terms of the kappa coefficient and greatly reduce run time regardless of the type of images used. Such enhancement reflects the robustness and superiority of the proposed method.

Highlights

Change detection intends to identify the changes between a given image pair of the same scene acquired at different times [1]
To validate the proposed change detection method for heterogeneous images and for homogeneous images, we used the following four datasets in the experiments: homogeneous synthetic aperture radar (SAR) images, homogeneous optical images, and two sets of heterogeneous images acquired by SAR and optical sensors
The result of hierarchical extreme learning machine classification (HELMC) was the least noisy and was closest to the reference image. This result was due to the separable sample selection method of HELMC

Summary

Introduction

Change detection intends to identify the changes between a given image pair of the same scene acquired at different times [1]. With the advancement of remote sensing technology, remote sensing data have developed into multi-temporal, multi-channel, and multi-source data that serve as the main source for detecting changes on the Earth’s surface. According to the characteristics of multi-temporal images, change detection methods can be divided into change detection with homogeneous or heterogeneous images. In change detection with homogeneous images, the multi-temporal images used for change detection are acquired from the same remote sensing sensor. Numerous methods have been proposed for change detection with homogeneous images. These change detection methods can be divided into pixel-based and object-based

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Conclusion