Assessing integration of intensity, polarimetric scattering, interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Jin, Huiran; Mountrakis, Giorgos; Stehman, Stephen V.

doi:10.1016/j.isprsjprs.2014.09.017

Cited by 51 publications

(22 citation statements)

References 81 publications

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“…Cohen & Goward, 2004;Friedl et al, 2010;Gómez, White, & Wulder, 2016;Hansen et al, 2013) and radar (e.g. Engdahl & Hyyppa, 2003;Miettinen & Liew, 2011;Jin, Mountrakis, & Stehman, 2014;Walker, Stickler, Kellndorfer, Kirsch, & Nepstad, 2010) satellite imagery for classification and the mapping of land cover-land use (LCLU).…”

Section: Introductionmentioning

confidence: 99%

Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

2017

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Land cover-land use (LCLU) classification tasks can take advantage of the fusion of radar and optical remote sensing data, leading generally to increase mapping accuracy. Here we propose a methodological approach to fuse information from the new European Space Agency Sentinel-1 and Sentinel-2 imagery for accurate land cover mapping of a portion of the Lower Magdalena region, Colombia. Data pre-processing was carried out using the European Space Agency's Sentinel Application Platform and the SEN2COR toolboxes. LCLU classification was performed following an object-based and spectral classification approach, exploiting also vegetation indices. A comparison of classification performance using three commonly used classification algorithms was performed. The radar and visible-near infrared integrated dataset classified with a Support Vector Machine algorithm produce the most accurate LCLU map, showing an overall classification accuracy of 88.75%, and a Kappa coefficient of 0.86. The proposed mapping approach has the main advantages of combining the all-weather capability of the radar sensor, spectrally rich information in the visible-near infrared spectrum, with the short revisit period of both satellites. The mapping results represent an important step toward future tasks of aboveground biomass and carbon estimation in the region. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

2017

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“…Information from the microwave range of the electromagnetic spectrum may improve separability of such classes, as the geometry of objects with strong backscattering at buildings is fundamentally different from the weak backscatter of bare soils. Information from Synthetic Aperture Radar (SAR) images may also be helpful for mapping regions of frequent cloud cover, such as Colombia, as long waves penetrate clouds and have shown potential to improve forest classifications by going well into the canopy [30]. However, classifications from radar data alone have not shown promising results due to limited spectral resolution [31,32].…”

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confidence: 99%

Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series

2015

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Abstract:Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011¨2 years. Adding elevation to the feature set resulted in 70.50% accuracy.

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“…The random forests model is a bagging ensemble learning algorithm well-established in the literature of land use/cover classification [41][42][43]. Random forests models build numerous decision trees, and each tree is built using a random subset of independent variables and a random sample of the training dataset.…”

Section: Random Forests Modelmentioning

confidence: 99%

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

et al. 2017

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Abstract:In light of the need for fine-grained, accurate, and timely urban land use information, a per-field classification approach was proposed in this paper to automatically map fine-grained urban land use in a study area within Haidian District, Beijing, China, in 2016. High-resolution remote sensing imagery and multi-source social sensing data were used to provide both physical and socioeconomic information. Four categories of attributes were derived from both data sources for urban land use parcels segmented by the OpenStreetMap road network, including spectral/texture attributes, landscape metrics, Baidu Point-Of-Interest (POI) attributes, and Weibo attributes. The random forests technique was adopted to conduct the classification. The importance of each attribute, attribute category, and data source was evaluated for the classification as a whole and the classification of individual land use types. The results showed that a testing accuracy of 77.83% can be achieved. The approach is relatively good at classifying open space and residential parcels, and poor at classifying institutional parcels. While using solely remote sensing data or social sensing data can achieve equally high overall accuracy, their importance varies in terms of the classification of individual classes. Landscape metrics are the most important for open space parcels. Spectral/texture attributes are more important in identifying institutional and residential parcels. The classification of business parcels relies more on landscape metrics and social sensing data, and less on spectral/texture attributes. The classification accuracy can be potentially improved upon the acquisition of purer parcels and the addition of new attributes. It is expected that the proposed approach will be useful for the routine update of urban land use information and large-scale urban land use mapping.

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Assessing integration of intensity, polarimetric scattering, interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Cited by 51 publications

References 81 publications

Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: a case study in the lower Magdalena region, Colombia

Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series

The Combined Use of Remote Sensing and Social Sensing Data in Fine-Grained Urban Land Use Mapping: A Case Study in Beijing, China

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