Abstract
The process from leaf sprouting to senescence is a phenological response, which is caused by the effect of temperature and moisture on the physiological response during the life cycle of trees. Therefore, detecting newly grown leaves could be useful for studying tree growth or even climate change. This study applied several target detection techniques to observe the growth of leaves in unmanned aerial vehicle (UAV) multispectral images. The weighted background suppression (WBS) method was proposed in this paper to reduce the interference of the target of interest through a weighted correlation/covariance matrix. This novel technique could strengthen targets and suppress the background. This study also developed the sparse enhancement (SE) method for newly grown leaves (NGL), as sparsity has features similar to newly grown leaves. The experimental results suggested that using SE-WBS based algorithms could improve the detection performance of NGL for most detectors. For the global target detection methods, the SE-WBS version of adaptive coherence estimator (SE-WBS-ACE) refines the area under the receiver operating characteristic curve (AUC) from 0.9417 to 0.9658 and kappa from 0.3389 to 0.4484. The SE-WBS version of target constrained interference minimized filter (SE-WBS-TCIMF) increased AUC from 0.9573 to 0.9708 and kappa from 0.3472 to 0.4417; the SE-WBS version of constrained energy minimization (SE-WBS-CEM) boosted AUC from 0.9606 to 0.9713 and kappa from 0.3604 to 0.4483. For local target detection methods, the SE-WBS version of adaptive sliding window CEM (ASW SE-WBS-CEM) enhanced AUC from 0.9704 to 0.9796 and kappa from 0.4526 to 0.5121, which outperforms other methods.
Highlights
Protecting wild tree coverage for sustainable forest ecosystem resources is crucial to relieve the effects of global warming and climate change
This paper proposes SE-weighted background suppression (WBS) to increase the accuracy of target detection algorithms, in which an image is preprocessed by sparse enhancement to enhance newly grown leaves (NGL), highlight the difference between elements with different features, and observe whether or not preprocessing could increase the accuracy of the target detection
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Summary
Protecting wild tree coverage for sustainable forest ecosystem resources is crucial to relieve the effects of global warming and climate change. Newly grown tree leaves can be regarded as the priority target of a tree’s response to temperature change and could provide key information for the early detection of climate change [5]. Active target detection algorithms such as constrained energy minimization (CEM) [25,26], the adaptive coherence estimator (ACE) [27,28], the target-constrained interference-minimized filter (TCIMF) [29], and adaptive window-based constrained energy minimization (AW-CEM) [30] all require a certain level of prior knowledge to locate specific targets of interest. CEM and ACE only require one target signature information; TCIMF can detect multiple targets and AW-CEM can adjust window size to detect small targets
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