isprs annals III 3 457 2016
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Validation was made by comparing the classification results with the ground reference data, which yielded satisfactory agreement with overall accuracy 84.1% and Kappa
Additionally, it is evaluated how hyperspectral vegetation indices like the NDVI are effected by the angular effects within a single image and if the viewing geometry influences
We have demonstrated an object-based classification of high- value crops using an optimized SVM model using LiDAR data and Orthophoto. There are three major parts of the presented
In this work, a set of SPOT-7 multispectral images is used to test the performance of soft classification methods and sub-pixel mapping methods for the classification
Second, data sets and experiments are described: experiments were performed on data sets generated from material reference reflectance spectra from available spectral libraries..
To efficiently represent the spatial-spectral feature information around the central pixel within a neighbourhood window, the unsupervised convolutional sparse
These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web
The major improvements by taking not only a single band into account as typically realized, but multiple bands combined with additional external geo-information data such as of
