Research on Feature Selection Method Oriented to Crop Identification Using Remote Sensing Image Classification

An, Qi; Gao, Wanlin; Yang, Bian; Wu, Jiaying; Yu, Lina; Liu, Zili

doi:10.1109/fskd.2009.488

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…The opening of the Landsat archives in 2008 has pushed forward the implementation of data analysis and image classification techniques based on multi-temporal features and time series analysis [17]. Multi-temporal analysis techniques have been applied as well to coarser resolution data such as NOAA-AVHRR [18] and NASA-MODIS data [1,19], taking advantage of high revisit time for these sensors [20]. Other satellite data too, with spectral and spatial features similar to Landsat, have been used for crop mapping achieving satisfactory results, e.g., IRS LISS data [21,22].…”

Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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Abstract:The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.

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Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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“…It develops efficient, fast agricultural remote sensing monitoring data early warning business throughout the country, offering timely, and objectively agricultural remote sensing monitoring data resource for overall policy decision [7]. Currently, our country has established agricultural remote sensing monitoring business network of which is centered in Remote center of ministry of agriculture, supported by area centers and based on ground observation cities, as shown in Figure 1.…”

Section: Analysis Of Agricultural Remote Sensing Monitoring Data Resomentioning

confidence: 98%

Research on Grid Framework of Agricultural Remote Sensing Monitoring Data Sharing

Gao¹,

An²,

Yu³

et al. 2012

Intelligent Automation & Soft Computing

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In order to effectively implement sharing and integrated application of agricultural remote monitoring data, which is of distributed areas, various types, different systems and a large amount of data, across different areas, departments and systems, new networked framework is needed to be introduced to promote sharing of resources. In this paper, based on grid, considering the current situation of agricultural remote sensing monitoring data, Grid Framework of Agricultural Remote Sensing Monitoring Data Sharing is proposed, which is designed from both the logical and physical aspect. With the explanation of its components and the relationship among them, it offers well high level instruction supporting framework for agricultural remote monitoring data. Meanwhile, based on the framework, agricultural remote monitoring data sharing grid prototype system is built in the paper, after implementing the system, the difficulties of data sharing is overcame, and the efficiency is improved, data resource distributed in different departments is regularly integrated, the results is good and the system is feasible and practicable.

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M-A model of agricultural remote sensing monitoring metadata based on grid environment

Gao

et al. 2011

Mathematical and Computer Modelling

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Research on Feature Selection Method Oriented to Crop Identification Using Remote Sensing Image Classification

Cited by 8 publications

References 8 publications

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

Research on Grid Framework of Agricultural Remote Sensing Monitoring Data Sharing

M-A model of agricultural remote sensing monitoring metadata based on grid environment

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