التفاصيل البيبلوغرافية
| العنوان: |
Change Detection for Heterogeneous Remote Sensing Images with Improved Training of Hierarchical Extreme Learning Machine (HELM). |
| المؤلفون: |
Han, Te, Tang, Yuqi, Yang, Xin, Lin, Zefeng, Zou, Bin, Feng, Huihui |
| المصدر: |
Remote Sensing; Dec2021, Vol. 13 Issue 23, p4918, 1p |
| مصطلحات موضوعية: |
MACHINE learning, REMOTE sensing, SYNTHETIC aperture radar, PROBLEM solving, OPTICAL images |
| الشركة/الكيان: |
SENTINEL-1 (Artificial satellite) |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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