New publication on decision fusion

New publication on decision fusion

August 1, 2015

Decision_fusion_loewOur new publication in ISPRS is accepted: “Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data”.

This study addressed the classification of multi-temporal satellite data from RapidEye by considering different classifier algorithms and decision fusion. Four non-parametric classifier algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), were applied to map crop types in various irrigated landscapes in Central Asia. A novel decision fusion strategy to combine the outputs of the classifiers was proposed. This approach is based on randomly selecting subsets of the input data-set and aggregating the probabilistic outputs of the base classifiers with another meta-classifier. During the decision fusion, the reliability of each base classifier algorithm was considered to exclude less reliable inputs at the class-basis. The spatial and temporal transferability of the classifiers was evaluated using data sets from four different agricultural landscapes with different spatial extents and from different years. A detailed accuracy assessment showed that none of the stand-alone classifiers was the single best performing. Despite the very good performance of the base classifiers, there was still up to 50% disagreement in the maps produce by the two single best classifiers, RF and SVM. The proposed fusion strategy, however, increased overall accuracies up to 6%. In addition, it was less sensitive to reduced training set sizes and produced more realistic land use maps with less speckle. The proposed fusion approach was better transferable to data sets from other years, i.e. resulted in higher accuracies for the investigated classes. The fusion approach is computationally efficient and appears well suited for mapping diverse crop categories based on sensors with a similar high repetition rate and spatial resolution like RapidEye, for instance the upcoming Sentinel-2 mission.

 

you may also like:

Our research covered in various news channels

Our research covered in various news channels

Our joint research with various other disciplines got covered by various radio stations such as: Deutschlandfunk: https://www.deutschlandfunk.de/urbane-hitzeinseln-vermeiden-warum-wuerzburg-jetzt-per-lastenrad-kartiert-wird-dlf-f74d8b70-100.htmlBayern 2:...

social event – joint bouldering

social event – joint bouldering

Our Phd student Henri and one of our student assistants, Stefan, organized a joint bouldering sessions and booked the bouldering gym RockIn exclusively for us. It was great having the whole place for us to climb, chat and just enjoy a day with the colleagues and...

Presentations at the EARSeL conference in Manchester

Presentations at the EARSeL conference in Manchester

Dr. Marta Sapena and Dr. John Friesen represented the Earth Observation Center (EOC) of the German Aerospace Center (DLR) and our Earth Observation Research Cluster (EORC) this week at the EARSeL conference in Manchester (https://manchester2024.earsel.org/).  They...

Our PhD candidate Ines Standfuss teaches at AniMove

Our PhD candidate Ines Standfuss teaches at AniMove

Our PhD candidate Ines Standfuss is teaching remote sensing for animal movement analysis this year at MPI at Lake Constanze. The AniMove science school has been founded more than ten years ago together with MPI and other organisations such as Smithsonian joined in the...

television and radio coverage about urban measurements

television and radio coverage about urban measurements

Our urban research got covered by TV and radio where we had the chance to explain the relevance of urban monitoring via remote sensing methods as well as in-situ devices (in cooperation with Prof. Marco Schmidt) especially for adaptation and mitigation potential of...