Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images

Lu, Xiaochen; Zhang, Junping; Li, Tong; Zhang, Ye

doi:10.3390/rs8100804

Cited by 13 publications

(5 citation statements)

References 39 publications

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“…In the last few years, progressive development of AL resulted in various regression strategies. In this respect, mainly two query frameworks were adapted to solve problems for EO data analysis ( Verrelst et al, 2016 ; Pasolli et al, 2012 ): (1) uncertainty sampling, e.g., He et al (2014) or Douak et al (2011) , and (2) diversity methods, e.g., Lu et al (2016) . Sampling based on uncertainty heuristics belongs to the most traditional approaches.…”

Section: Methodsmentioning

confidence: 99%

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Estévez

Salinero-Delgado

Berger

et al. 2022

Remote Sensing of Environment

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Section: Methodsmentioning

confidence: 99%

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Estévez

Salinero-Delgado

Berger

et al. 2022

Remote Sensing of Environment

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“…Two families or query frameworks were adapted to solve regression problems for Earth observation data analysis [ 33 , 41 ]: (1) uncertainty, e.g., [ 79 ] and (2) diversity, e.g., [ 80 ].…”

Section: Background: Active Learning Theorymentioning

confidence: 99%

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

et al. 2021

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The current exponential increase of spatiotemporally explicit data streams from satellite-based Earth observation missions offers promising opportunities for global vegetation monitoring. Intelligent sampling through active learning (AL) heuristics provides a pathway for fast inference of essential vegetation variables by means of hybrid retrieval approaches, i.e., machine learning regression algorithms trained by radiative transfer model (RTM) simulations. In this study we summarize AL theory and perform a brief systematic literature survey about AL heuristics used in the context of Earth observation regression problems over terrestrial targets. Across all relevant studies it appeared that: (i) retrieval accuracy of AL-optimized training data sets outperformed models trained over large randomly sampled data sets, and (ii) Euclidean distance-based (EBD) diversity method tends to be the most efficient AL technique in terms of accuracy and computational demand. Additionally, a case study is presented based on experimental data employing both uncertainty and diversity AL criteria. Hereby, a a simulated training data base by the PROSAIL-PRO canopy RTM is used to demonstrate the benefit of AL techniques for the estimation of total leaf carotenoid content (Cxc) and leaf water content (Cw). Gaussian process regression (GPR) was incorporated to minimize and optimize the training data set with AL. Training the GPR algorithm on optimally AL-based sampled data sets led to improved variable retrievals compared to training on full data pools, which is further demonstrated on a mapping example. From these findings we can recommend the use of AL-based sub-sampling procedures to select the most informative samples out of large training data pools. This will not only optimize regression accuracy due to exclusion of redundant information, but also speed up processing time and reduce final model size of kernel-based machine learning regression algorithms, such as GPR. With this study we want to encourage further testing and implementation of AL sampling methods for hybrid retrieval workflows. AL can contribute to the solution of regression problems within the framework of operational vegetation monitoring using satellite imaging spectroscopy data, and may strongly facilitate data processing for cloud-computing platforms.

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“…As described in [34], active learning techniques can be categorized based on uncertainty [44] or diversity criteria [45]. In the first case, samples are ranked according to their uncertainty.…”

Section: Methodsmentioning

confidence: 99%

Forest Aboveground Biomass Estimation Using Machine Learning Ensembles: Active Learning Strategies for Model Transfer and Field Sampling Reduction

Amitrano,

Giacco,

Marrone

et al. 2023

Remote Sensing

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Biomass is a crucial indicator of the carbon sequestration capacity of a vegetation ecosystem. Its dynamic is of interest because it impacts on the carbon cycle, which plays an important role in the global climate and its changes. This work presents a novel technique, able to transfer a calibrated regression model between different areas by exploiting an active learning methodology and using Shannon’s entropy as a discriminator for sample selection. Model calibration is performed based on a reference area for which an extended ground truth is available and implemented via regression bootstrap. Then, re-calibration samples for model transfer are selected through active learning, allowing for choosing a limited number of points to be investigated for training data collection. Different sampling strategies and regression techniques have been tested to demonstrate that a significant reduction in the number of calibration samples does not affect the estimation performance. The proposed workflow has been tested on a dataset concerning Finnish forests. Experimental results show that the joint exploitation of regression ensembles and active learning dramatically reduces the amount of field sampling, providing aboveground biomass estimates comparable to those obtained using literature techniques, which need extended training sets to build reliable predictions.

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Incorporating Diversity into Self-Learning for Synergetic Classification of Hyperspectral and Panchromatic Images

Cited by 13 publications

References 39 publications

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data

A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data

Forest Aboveground Biomass Estimation Using Machine Learning Ensembles: Active Learning Strategies for Model Transfer and Field Sampling Reduction

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