CLASSIFICATION ACCURACY INCREASE USING MULTISENSOR DATA FUSION

Classification Accuracy Increase Using Multisensor Data Fusion

The practical use of very high resolution visible and near-infrared (VNIR) data is still growing (IKONOS, Quickbird, GeoEye-1, etc.) but for classification purposes the number of bands is limited in comparison to full spectral imaging. These limitations may lead to the confusion of materials such as different roofs, pavements, roads, etc. and therefore may provide wrong interpretation and use o...

Multisensor data fusion using fuzzy concepts

In this study, a fuzzy-based multisensor data fusion classifier is developed and applied to land cover classification using ERS-1/JERS-1 SAR composites. This classifier aims at the integration of multisensor and contextual information in a single and a homogeneous framework. Initial Fuzzy Membership Maps (FMM) to different thematic classes are first calculated using classes and sensors a priori...

Reactive Tuning of Target Estimate Accuracy in Multisensor Data Fusion

Dealing with conflicting and target-specific requirements is an important issue in multi-sensor and multi-target tracking. This paper aims to allocate sensing resources among various targets in reaction to individual information requests. The approach proposed is to introduce agents for every relevant target responsible for its tracking. Such agents are expected to bargain with each other for a...

Multisensor Data Fusion

Multisensor Data Fusion Using Multiresolusion Analysis (mra)

The objective of this study is to propose multiresolution data fusion scheme using multiresolution analysis (MRA). Three techniques compared, respectively based on Haar wavelet basis (HWB), Daubechies wavelet basis (DWB), and IHS transform. Daubechies Wavelet Basis (DWB) is far more efficient than the HWB and IHS transform, thus establishing the advantages for data fusion of formally multiresol...