Classification of remote sensing imagery with high spatial resolution

Fauvel, Mathieu; Palmason, J.A.; Benediktsson, Jón Atli; Chanussot, Jocelyn; Sveinsson, Jóhannes R.

doi:10.1117/12.637224

Cited by 3 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, remote sensing images with different spatial resolutions have their own applicability and limitations in the crop planting classification [57,58]. Remote sensing images with low spatial resolution have high temporal resolution and can cover a large region, but limited by the spatial resolution, there are many mixed pixels, and they can only be applied to extract crop planting areas roughly.…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Extracting Winter Wheat Planting Area Based on Ownership Parcel Vector Data and Medium-Resolution Remote Sensing Images

Xie

Zhang

et al. 2021

Journal of Mathematics

View full text Add to dashboard Cite

In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60 km2 in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa = 0.9279, overall accuracy = 96.41%, user’s accuracy = 99.16%, producer’s accuracy = 93.39%, commission errors = 0.84%, and omission errors = 6.61%) when the size of ownership parcels matches the spatial resolution of remote sensing images.

show abstract

Section: Introductionmentioning

confidence: 99%