The CCRS SAR/MSS Anderson River Data Set

IEEE Trans. Geosci. Remote Sensing

2002

275

121

Abstract-The combination of multisource remote sensing and geographic data is believed to offer improved accuracies in land cover classification. For such classification, the conventional parametric statistical classifiers, which have been applied successfully in remote sensing for the last two decades, are not appropriate, since a convenient multivariate statistical model does not exist for the data. In this paper, several single and multiple classifiers, that are appropriate for the classification of multisource remote sensing and geographic data are considered. The focus is on multiple classifiers: bagging algorithms, boosting algorithms, and consensus-theoretic classifiers. These multiple classifiers have different characteristics. The performance of the algorithms in terms of accuracies is compared for two multisource remote sensing and geographic datasets. In the experiments, the multiple classifiers outperform the single classifiers in terms of overall accuracies.

Section: B Anderson River Datasetmentioning

confidence: 93%

“…In the second experiment, the Anderson River dataset, which is a multisource remote sensing and geographic dataset made available by the Canada Centre for Remote Sensing (CCRS) [21], was used. This dataset is very difficult to classify [6] due to a number of mixed forest type classes.…”

Section: B Anderson River Datasetmentioning

confidence: 99%

Multiple classifiers applied to multisource remote sensing data

Briem

IEEE Trans. Geosci. Remote Sensing

2002

275

121

“…The data used in the second experiment, the Anderson River data set, are a multisource remote sensing and geographic data set made available by the Canada Centre for Remote Sensing (CCRS), Ottawa, Ontario [32]. The imagery involves a 2.8 km by 2.8 km forestry site in the Anderson River area of British Columbia, Canada.…”

Section: B Experiments 2: Anderson River Data Setmentioning

confidence: 99%

Parallel consensual neural networks

IEEE Trans. Neural Netw.

Ersoy

et al. 1997

151

Abstract-A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

“…The data used in the second experiment, the Anderson River data set, are a multisource remote sensing and geographic data set made available by the Canada Centre for Remote Sensing (CCRS) [32]. This data set is very difficult to classify.…”

Section: B Experiments 2: Anderson River Data Setmentioning

confidence: 99%

“…The area is characterized by rugged topography, with terrain elevations ranging from 330 m to 1100 m above sea level. The forest cover is primarily coniferus, with Douglas fir predominating up to approximately 1050 m elevation, and cedar, hemlock and spruce types predominating at higher elevations [32]. Six data sources were used: 1) Airborne Multispectral Scanner (AMSS) with 11 spectral data channels (ten channels from 380 to 1100 nm and one channel from 8 to 14 m).…”

Section: B Experiments 2: Anderson River Data Setmentioning

confidence: 99%

Hybrid consensus theoretic classification

IEEE Trans. Geosci. Remote Sensing

Swain

1997

Abstract-Hybrid classification methods based on consensus from several data sources are considered. Each data source is at first treated separately and modeled using statistical methods. Then weighting mechanisms are used to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and nonlinear optimization methods are considered and used in classification of two multisource remote sensing and geographic data sets. A nonlinear method which utilizes a neural network gives excellent experimental results. The hybrid statistical/neural method outperforms all other methods in terms of test accuracies in the experiments.