Version 1
: Received: 9 June 2023 / Approved: 9 June 2023 / Online: 9 June 2023 (08:10:25 CEST)
How to cite:
Zhu, Y.; Li, Q.; Lv, Z.; Falco, N. Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images. Preprints2023, 2023060682. https://doi.org/10.20944/preprints202306.0682.v1
Zhu, Y.; Li, Q.; Lv, Z.; Falco, N. Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images. Preprints 2023, 2023060682. https://doi.org/10.20944/preprints202306.0682.v1
Zhu, Y.; Li, Q.; Lv, Z.; Falco, N. Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images. Preprints2023, 2023060682. https://doi.org/10.20944/preprints202306.0682.v1
APA Style
Zhu, Y., Li, Q., Lv, Z., & Falco, N. (2023). Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images. Preprints. https://doi.org/10.20944/preprints202306.0682.v1
Chicago/Turabian Style
Zhu, Y., Zhiyong Lv and Nicola Falco. 2023 "Novel Land Cover Change Detection Deep Learning Framework with Very Small Initial Samples Using Heterogeneous Remote Sensing Images" Preprints. https://doi.org/10.20944/preprints202306.0682.v1
Abstract
Change detection with heterogeneous remote sensing images (Hete-CD) plays an important role in practical applications, especially when homogenous remote sensing images are unavailable. However, bitemporal heterogeneous remote sensing images (HRSIs) cannot compare directly to measure change magnitude, and many deep learning methods require large amounts of samples to train the module. Moreover, labeling many samples for land cover change detection with HRSIs is time-consuming and labor-intensive. Therefore, acquiring satisfactory performance of Hete-CD remains a challenge for deep learning networks with very small training samples. In this study, we promote a novel deep-learning framework for Hete-CD to obtain satisfactory performance while the initial samples are very small. We initially design a multiscale network with select kernel-attention module to focus on capturing different change targets with various sizes and shapes. Then, a simple yet effective non-parametric sample-enhanced algorithm based on the Pearson correlation coeffi-cient is promoted to explore potential samples around each initial sample. Finally, the proposed network and sample-enhanced algorithm are fused into one iterative framework to improve the change detection performance with very small samples. Experimental results conducted on four pairs of actual HRSIs indicated that the proposed framework can achieve competitive accuracies with very small samples for initialization when compared with some state-of-the-art methods. For example, the improvement is approximately 3.38% and 1.99% when compared with the selected traditional methods and deep learning methods, respectively.
Keywords
Land cover change detection; HRSIs; Deep learning neural network; Very Small training samples
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
