We are glad to co-author a new publication titled ‚Object-based post-processing method for crop classification“ of our colleagues from the Center for Remote Sensing of Land Surfaces (ZFL) in Bonn (Germany), and the Space Research Institute NASU-SSAU (Ukraine).
In this paper, we propose a novel method for an object-based post-classification filtering, specifically tailored to improve agricultural land use maps. That has significant impact on the solving other applied tasks like detection of land cover changes and crop rotation violation, area estimation and crop yield forecasting. The main idea of this method is to divide classification map into separate objects (group of pixels with the same class value) and investigate the properties of them, taking into account the specificity of each class, independently. The most challenging task in post-classification filtering is preserving edges and boundaries between different fields. Often these boundaries are narrow and some traditional filters tend to treat this like noise and remove them. To deal with this, our method identifies boundaries of objects like crop fields, based on a modified version of the Sobel algorithm. The accuracy and effectiveness of our method has been tested and compared with other methods, based on accuracy assessments and visual comparison.
More information on many aspects of IGARSS 2018 can be found here: https://igarss2018.org/
Source of image: Center for Remote Sensing of Land Surfaces (ZFL), Mr. Mykola Lavreniuk