Seamless change detection and mosaicing for aerial imagery

Nimisha, T M; Rajagopalan, A. N.; Aravind, R.

doi:10.1109/cvprw.2015.7301393

Cited by 1 publication

(1 citation statement)

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“…For example, affected by external conditions (e.g., illumination, sensor, and shooting angle), the visual appearance of the same/different foreground objects may be dissimilar/similar in bi-temporal images. At first, traditional machine learning techniques are popular for image change detection [37,35,13]. However, the low-level visual features limit their performance.…”

Section: Introductionmentioning

confidence: 99%

SoftMatch Distance: A Novel Distance for Weakly-Supervised Trend Change Detection in Bi-Temporal Images

Yang¹,

Xu²,

Zhang³

et al. 2023

Preprint

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General change detection (GCD) and semantic change detection (SCD) are common methods for identifying changes and distinguishing object categories involved in those changes, respectively. However, the binary changes provided by GCD is often not practical enough, while annotating semantic labels for training SCD models is very expensive. Therefore, there is a novel solution that intuitively dividing changes into three trends ("appear", "disappear" and "transform") instead of semantic categories, named it trend change detection (TCD) in this paper. It offers more detailed change information than GCD, while requiring less manual annotation cost than SCD. However, there are limited public data sets with specific trend labels to support TCD application. To address this issue, we propose a softmatch distance which is used to construct a weakly-supervised TCD branch in a simple GCD model, using GCD labels instead of TCD label for training. Furthermore, a strategic approach is presented to successfully explore and extract background information, which is crucial for the weaklysupervised TCD task. The experiment results on four public data sets are highly encouraging, which demonstrates the effectiveness of our proposed model.

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Section: Introductionmentioning

confidence: 99%