Information mining in remote sensing image archives: system evaluation

Daschiel, H.; Datcu, Mihai

doi:10.1109/tgrs.2004.838374

Cited by 66 publications

(23 citation statements)

References 8 publications

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“…Moreover, training data quality is highly correlated with the approach of im interpretation. As the ability of imaging spectroscopy to provide vegetation signal is limited at l vegetation fractional cover, efforts were made by using a human-machine interactive classificat method, which makes it easy to bring technological experience and expertise into the classificat process [34]. In this study, in spite of the mean and standard deviation of the confused-p overestimates, 10 of 13 ional maps of fractional woody and herbaceous e approach that is developed and evaluated for data from high-resolution images and predictor Q1 product and ancillary variables, including ation.…”

Section: Discussionmentioning

confidence: 99%

An Improved Estimation of Regional Fractional Woody/Herbaceous Cover Using Combined Satellite Data and High-Quality Training Samples

Liu

Qiu

et al. 2017

Remote Sensing

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Abstract:Mapping vegetation cover is critical for understanding and monitoring ecosystem functions in semi-arid biomes. As existing estimates tend to underestimate the woody cover in areas with dry deciduous shrubland and woodland, we present an approach to improve the regional estimation of woody and herbaceous fractional cover in the East Asia steppe. This developed approach uses Random Forest models by combining multiple remote sensing data-training samples derived from high-resolution image in a tailored spatial sampling and model inputs composed of specific metrics from MODIS sensor and ancillary variables including topographic, bioclimatic, and land surface information. We emphasize that effective spatial sampling, high-quality classification, and adequate geospatial information are important prerequisites of establishing appropriate model inputs and achieving high-quality training samples. This study suggests that the optimal models improve estimation accuracy (NMSE 0.47 for woody and 0.64 for herbaceous plants) and show a consistent agreement with field observations. Compared with existing woody estimate product, the proposed woody cover estimation can delineate regions with subshrubs and shrubs, showing an improved capability of capturing spatialized detail of vegetation signals. This approach can be applicable over sizable semi-arid areas such as temperate steppes, savannas, and prairies.

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Section: Discussionmentioning

confidence: 99%

An Improved Estimation of Regional Fractional Woody/Herbaceous Cover Using Combined Satellite Data and High-Quality Training Samples

Liu

Qiu

et al. 2017

Remote Sensing

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“…Precision is defined as the fraction of the retrieved images that are relevant to the query, and recall as the fraction of the total number of relevant images (contained in the archive) that are retrieved [2]:…”

Section: Methodology For the Contrast Of Eiasmentioning

confidence: 99%

On the Use of a Hybrid Approach to Contrast Endmember Induction Algorithms

Veganzones

Hernández

2010

Lecture Notes in Computer Science

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Abstract. In remote sensing hyperspectral image processing, identifying the constituent spectra (endmembers) of the materials in the image is a key procedure for further analysis. The contrast between Endmember Inductions Algorithms (EIAs) is a delicate issue, because there is a shortage of validation images with accurate ground truth information, and the induced endmembers may not correspond to any know material, because of illumination and atmospheric effects. In this paper we propose a hybrid validation method, composed on a simulation module which generates the validation images from stochastic models and evaluates the EIA through Content Based Image Retrieval (CBIR) on the database of simulated hyperspectral images. We demonstrate the approach with two EIA selected from the literature.

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“…In this context, knowledge discovery technologies are an important alternative to explore and find relevant information on this huge volume of data. Some initiatives involving data and image mining have been accomplished through different techniques with reasonable results [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Monitoring in Regional Scale Using Clustering on Satellite Image Time Series

Gonçalves¹,

Zullo²,

Amaral³

et al. 2018

Time Series Analysis and Applications

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The remote sensing images are more accessible nowadays and there are proper technologies to receive, distribute, manipulate and process long satellite image time series that can be used to improve traditional methods for harvest monitoring and forecasting. The potential of the satellite multi-temporal images to support research of agricultural monitoring has increased according to improvements in technological development, especially in analysis of large volume of data available for knowledge discovery. In Brazil, sugarcane is cultivated on extensive fields and is the main agriculture crop used to produce ethanol. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis with multi-temporal satellite images having low spatial resolution. A large database of this kind of image and specific software were used to perform the image preprocessing phase, extract time series, apply clustering method and enable the data visualization on several steps during the whole analysis process. According to the analysis done, our methodology allows to identify land areas with similar development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution can indeed be satisfactorily used in agricultural crop monitoring in regional scale.

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Information mining in remote sensing image archives: system evaluation

Cited by 66 publications

References 8 publications

An Improved Estimation of Regional Fractional Woody/Herbaceous Cover Using Combined Satellite Data and High-Quality Training Samples

An Improved Estimation of Regional Fractional Woody/Herbaceous Cover Using Combined Satellite Data and High-Quality Training Samples

On the Use of a Hybrid Approach to Contrast Endmember Induction Algorithms

Agricultural Monitoring in Regional Scale Using Clustering on Satellite Image Time Series

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