L8 Biome Cloud Validation Masks

Foga, Steven; Dwyer, John

doi:10.5066/f7251gdh

Cited by 12 publications

(4 citation statements)

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“…In this work, we use three of them that have a large coverage of acquisitions across different dates, latitudes and landscapes. The Biome dataset [57], released for the Landsat-8 validation study of Foga et. al [58], is the largest among them.…”

Section: Generative Adversarial Domain Adaptationmentioning

confidence: 99%

“…The SPARCS dataset, collected in the study of Hughes and Hayes [59], contains 80 1,000×1,000 patches from different Landsat-8 acquisitions. Finally, the 38-Clouds dataset of Mohajerani and Saeedi [18] has 38 full scenes mostly located in North PV24 [5] Biome [57] 38-Clouds [18] SPARCS [24] Fig. 6.…”

Section: Generative Adversarial Domain Adaptationmentioning

confidence: 99%

“…Location of Landsat-8 and Proba-V products with manually annotated ground truth cloud mask. For the Biome [57] and 38-Clouds dataset [18] we additionally downloaded Proba-V images from the same location and acquisition time if available.…”

Section: Generative Adversarial Domain Adaptationmentioning

confidence: 99%

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Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

Mateo-García¹,

Laparra²,

López-Puigdollers³

et al. 2020

Preprint

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The number of Earth observation satellites carrying optical sensors with similar characteristics is constantly growing. Despite their similarities and the potential synergies among them, derived satellite products are often developed for each sensor independently. Differences in retrieved radiances lead to significant drops in accuracy, which hampers knowledge and information sharing across sensors. This is particularly harmful for machine learning algorithms, since gathering new ground truth data to train models for each sensor is costly and requires experienced manpower. In this work, we propose a domain adaptation transformation to reduce the statistical differences between images of two satellite sensors in order to boost the performance of transfer learning models. The proposed methodology is based on the Cycle Consistent Generative Adversarial Domain Adaptation (CyCADA) framework that trains the transformation model in an unpaired manner. In particular, Landsat-8 and Proba-V satellites, which present different but compatible spatio-spectral characteristics, are used to illustrate the method. The obtained transformation significantly reduces differences between the image datasets while preserving the spatial and spectral information of adapted images, which is hence useful for any general purpose cross-sensor application. In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function. Results show that, when the proposed transformation is applied, cloud detection models trained in Landsat-8 data increase cloud detection accuracy in Proba-V.

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Section: Generative Adversarial Domain Adaptationmentioning

confidence: 99%

Section: Generative Adversarial Domain Adaptationmentioning

confidence: 99%

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Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

Mateo-García¹,

Laparra²,

López-Puigdollers³

et al. 2020

Preprint

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show abstract

“…They test their method on four Landsat-8 datasets, including their own 35-/95cloud datasets, which consist of image subsets with four spectral bands ("Blue", "Green", "Red", "NIR") and corresponding cloud annotations. Similar to the L8-Biome (Scaramuzza et al, 2016) dataset, cloud shadows are not consistently annotated. Despite the promising results for cloud detection, only few studies and datasets specifically consider cloud shadows.…”

Section: Introductionmentioning

confidence: 99%

Ukis-Csmask: A Python Package for Multi-Sensor Cloud and Cloud Shadow Segmentation

Wieland

Fichtner

Martinis

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Cloud and cloud shadow segmentation is a crucial pre-processing step for any application that uses multi-spectral satellite images. In particular, time-critical disaster applications, require accurate and immediate cloud and cloud shadow masks while being able to adapt to possibly large variations caused by different sensor characteristics, scene properties or atmospheric conditions. This study introduces the newly developed open-source Python package ukis-csmask for cloud and cloud shadow segmentation in multi-spectral satellite images. Segmentation with ukis-csmask is performed with a pre-trained Convolutional Neural Network based on a U-Net architecture. It works directly on Level-1C data, eliminating the need for prior atmospheric correction. Images need to be in top of atmosphere reflectance and include at least the Blue, Green, Red, NIR, SWIR1 and SWIR2 spectral bands. We provide a performance evaluation on a recent benchmark dataset for cloud and cloud shadow segmentation and proof the generalization ability of our method across multiple satellites (Landsat-5, Landsat-7, Landsat-8, Landsat-9 and Sentinel-2). We also show the influence of augmentation and image bands on the segmentation performance and compare it to the widely used Fmask algorithm and a Random Forest classifier. Compared to previous work in this direction, our study focuses on multi-sensor generalization ability, simplicity and efficiency and provides a ready-to-use software package that has been thoroughly tested.

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An enhanced 3D model and generative adversarial network for automated generation of horizontal building mask images and cloudless aerial photographs

Ikeno

Fukuda

Yabuki

2021

Advanced Engineering Informatics

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L8 Biome Cloud Validation Masks

Cited by 12 publications

References 0 publications

Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud Detection

Ukis-Csmask: A Python Package for Multi-Sensor Cloud and Cloud Shadow Segmentation

An enhanced 3D model and generative adversarial network for automated generation of horizontal building mask images and cloudless aerial photographs

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