Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

Hu, Cong; Huo, Lianzhi; Zhang, Zheng; Tang, Ping

doi:10.1109/access.2020.2979291

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…If there is no cloud and shadow mask, cloud and shadow detection has to be conducted beforehand. Then, we use the information transfer method proposed in [50] to recover contaminated pixels. For each cloud or shadow contaminated image patch, the method searches for a similar image patch from the same geographical location in other images of the SITS, and then Poisson blending [57] is employed to make image patches transferred from other images fit seamlessly into the current image.…”

Section: Seeded Classification Of Sitsmentioning

confidence: 99%

“…To demonstrate the performance of our method, two SITS datasets of full-size Landsat 8 images from year 2013 to 2017 were constructed. Cloud and cloud shadow pixels were mended by information transfer [49,50] from other images in the same SITS to ensure that the temporal information was continuous enough to reflect the temporal evolution of land cover types. Compared with several benchmark methods that are still usable with few labeled samples, our method exhibits improved accuracy, and compared with raw DTW, our method can save approximately half of the time.…”

Section: Introductionmentioning

confidence: 99%

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Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping

Zheng

Tang

et al. 2022

Remote Sensing

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Satellite Image Time Series (SITS) record the continuous temporal behavior of land cover types and thus provide a new perspective for finer-grained land cover classification compared with the usual spectral and spatial information contained in a static image. In addition, SITS data is becoming more accessible in recent years due to newly launched satellites and accumulated historical data. However, the lack of labeled training samples limits the exploration of SITS data, especially with sophisticated methods. Even with a straightforward classifier, such as k-nearest neighbor, the accuracy and efficiency of the SITS similarity measure is also a pending problem. In this paper, we propose SKNN-LB-DTW, a seeded SITS classification method based on lower-bounded Dynamic Time Warping (DTW). The word “seeded” indicates that only a few labeled samples are required, and this is not only because of the lack of labeled samples but also because of our aim to explore the rich information contained in SITS, rather than letting training samples dominate the classification results. We use a combination of cascading lower bounds and early abandoning of DTW as an accurate yet efficient similarity measure for large scale tasks. The experimental results on two real SITS datasets demonstrate the utility of the proposed SKNN-LB-DTW, which could become an effective solution for SITS classification when the amount of unlabeled SITS data far exceeds the labeled data.

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Section: Seeded Classification Of Sitsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping

Zheng

Tang

et al. 2022

Remote Sensing

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“…Figure 7 illustrates the location and overview of the study area. We first mended cloud and cloud shadow pixels in each raw Landsat 8 image by the method proposed in [14] and then selected 23 clean images to construct the dataset. Figure 8 shows the temporal coverage of the selected images.…”

Section: Poyang Lake Datasetmentioning

confidence: 99%

“…Compared with a single-scene image, SITS records the evolution of land cover types over time and this kind of temporal information is sometimes critical to make land cover types more distinguishable [8][9][10]. In addition, image preprocessing methods required by SITS analytics, such as geometric correction [11,12] and cloud removal [13,14], become more mature than before. Due to the above reasons, SITS analytics has attracted much attention in recent years and many applications have been developed to explore the rich information contained in SITS, for example, classification [15,16], clustering [1,17], class noise reduction [18], trend detection [19], disturbance detection [20], etc.…”

Section: Introductionmentioning

confidence: 99%

Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

et al. 2021

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Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised analysis methods need to be developed. Similarity measure is critical for clustering, however, currently established methods represented by Dynamic Time Warping (DTW) still exhibit several issues when coping with SITS, such as pathological alignment, sensitivity to spike noise, and limitation on capacity. In this paper, we introduce a new time series similarity measure method named time adaptive optimal transport (TAOT) to the application of SITS clustering. TAOT inherits several promising properties of optimal transport for the comparing of time series. Statistical and visual results on two real SITS datasets with two different settings demonstrate that TAOT can effectively alleviate the issues of DTW and further improve the clustering accuracy. Thus, TAOT can serve as a usable tool to explore the potential of precious SITS data.

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“…The approach increased the recovered area from the tampered image by reconstructing the relationship between each compression bit and each reference bit. In [26], the poisson blending algorithm was applied in the cloud removing algorithm for a large area of landsat data. The gradient-domain compositing method is accelerated by utilizing a quad-tree approach.…”

Section: Introductionmentioning

confidence: 99%

Modified iterative method with red-black ordering for image composition using poisson equation

Sunarto

2021

Bulletin EEI

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Image composition involves the process of embedding a selected region of the source image to the target image to produce a new desirable image seamlessly. This paper presents an image composition procedure based on numerical differentiation using the laplacian operator to obtain the solution of the poisson equation. The proposed method employs the red-black strategy to speed up the computation by using two acceleration parameters. The method is known as modified two-parameter over-relaxation (MTOR) and is an extension of the existing relaxation methods. The MTOR was extensively studied in solving various linear equations, but its usefulness in image processing was never explored. Several examples were tested to examine the effectiveness of the proposed method in solving the poisson equation for image composition. The results showed that the proposed MTOR performed faster than the existing methods.

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Multi-Temporal Landsat Data Automatic Cloud Removal Using Poisson Blending

Cited by 16 publications

References 22 publications

Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping

Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping

Satellite Image Time Series Clustering via Time Adaptive Optimal Transport

Modified iterative method with red-black ordering for image composition using poisson equation

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