ERS-1 Synthetic Aperture Radar Images Research Articles

A Markov random field model for classification of multisource satellite imagery

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery.

Observations on the sensitivity of ERS-1 SAR image intensity to changes in aboveground biomass in young loblolly pine forests

Abstract A study was conducted to evaluate the sensitivity of the microwave return recorded by the ERS-1 C-band synthetic aperture radar ( SAR) to changes in above-ground woody biomass in young loblolly pine forests ( dry weight biomass ⩾ 6kgm− 1 Radar backscattering coefficients were derived from ERS-1 SAR imagery collected over 15 test stands near Durham, North Carolina, United States. Ground reference data were collected in order to characterize the aboveground biomass of the pine trees in the test stands. Significant linear correlations atp = 000l or better) were found between the radar backscattering and the various components of biomass ( both wet and dry weight) of the pine canopy ( e.g., bole biomass, stem biomass, needle biomass, canopy biomass and total biomass). The linear correlation coefficients ranged between 087 and 0-93. These results demonstrate the potential for using the C-band ERS-1 SAR to monitor biomass changes during the early successional stages in temperate coniferous forests, as ...