A Badging System for Reproducibility and Replicability in Remote Sensing Research

Frery, Alejandro C.; Gómez, Luis; Medeiros, Antonio C.

doi:10.1109/jstars.2020.3019418

Cited by 31 publications

(8 citation statements)

References 15 publications

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“…The platform was IDL (Interactive Data Language) version 8.5. Following the reproducibility guidelines discussed by Frery et al (2020), we make the code and data available at https: //github.com/rogerionegri/FloatingReferences.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

Negri

Frery

2021

Pattern Anal Applic

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The Earth's environment is continually changing due to both human and natural factors. Timely identification of the location and kind of change is of paramount importance in several areas of application. Because of that, Remote Sensing change detection is a topic of great interest. The development of precise change detection methods is a constant challenge. This study introduces a novel unsupervised change detection method based on data clustering and optimization. The proposal is less dependent on radiometric normalization than classical approaches. We carried experiments with remote sensing images and simulated data sets to compare the proposed method with other unsupervised well-known techniques. At its best, the proposal improves by 50 % the accuracy concerning the second best technique. Such improvement is most noticeable with uncalibrated data. Experiments with simulated data reveal that the proposal is better than all other compared methods at any practical significance level. The results show the potential of the proposed method.

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Section: Experiments and Resultsmentioning

confidence: 99%

Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

Negri

Frery

2021

Pattern Anal Applic

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“…Readers can reproduce the experiments according to the materials we offered. We acknowledge that the article is reproducible in all its terms for at least five years and that the materials concerning this article will be publicly available permanently [65]. All materials concerning this article can be downloaded from https://github.com/AICyberTeam/ AIR-PolSAR-Seg.…”

Section: F Discussionmentioning

confidence: 99%

AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images

Wang

Zeng

Yan

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Polarimetric synthetic aperture radar (PolSAR) terrain segmentation is a fundamental research topic in PolSAR image interpretation. Recently, many studies have been investigated to handle this task. However, the existing data for PolSAR terrain segmentation have relatively limited scale and their scene complexity is relatively simple. These issues greatly restrict the development of algorithms. Therefore, there is a strong requirement for establishing a large-scale data set for terrain segmentation in complex-scene PolSAR images. In this paper, we present a benchmark data set containing a PolSAR amplitude image with a 9082×9805-pixel region and 2000 image patches with a size of 512×512 for PolSAR terrain segmentation, which is called AIR-PolSAR-Seg. We collect the PolSAR image with a resolution of 8 m from the GaoFen-3 satellite, and it is equipped with pixel-wise annotation which covers six categories. Compared with the previous data resources, AIR-PolSAR-Seg preserves some specific properties. First, AIR-PolSAR-Seg owns a large-size PolSAR image and provides a large quantity of image patches. It offers the research community a complete data resource with adequate training examples and reliable validation results. Second, AIR-PolSAR-Seg is established upon a PolSAR image with high scene complexity. This characteristic motivates robust and advanced segmentation approaches to facilitate complex-scene PolSAR image analysis. Based on AIR-PolSAR-Seg, three tasks are introduced: multi-category segmentation, water body segmentation, and building segmentation. Moreover, a performance analysis of traditional approaches and deep learning-based approaches are conducted, which can be regarded as baselines and provide references for future research.

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“…The experimental results validated that MCAN can accurately detect intricate oil spill regions with minimal training data. We have released our code for public evaluation in order to support reproducibility and replicability in remote sensing research [47].…”

Section: Discussionmentioning

confidence: 99%

Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training

Lyu

Frery

et al. 2021

Remote Sensing

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We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model’s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.

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A Badging System for Reproducibility and Replicability in Remote Sensing Research

Cited by 31 publications

References 15 publications

Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

Unsupervised Change Detection Driven by Floating References: A Pattern Analysis Approach

AIR-PolSAR-Seg: A Large-Scale Data Set for Terrain Segmentation in Complex-Scene PolSAR Images

Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training

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