In many remote-sensing applications, measured data are a strong function of the environment in which they are collected. This paper introduces a new context-dependent classification algorithm to address and exploit this phenomenon. Within the proposed framework, an ensemble of classifiers is constructed, each associated with a particular environment. The key to the method is that the relative importance of each object (i.e., data point) during the learning phase for a given classifier is controlled via a modulating factor based on the similarity of auxiliary environment features. Importantly, the number of classifiers to learn and all other associated model parameters are inferred automatically from the training data. The promise of the proposed method is demonstrated on classification tasks seeking to distinguish underwater targets from clutter in synthetic aperture sonar imagery. The measured data were collected with an autonomous underwater vehicle during several large experiments, conducted at sea between 2008 and 2012, in different geographical locations with diverse environmental conditions. For these data, the environment was quantified by features (extracted from the imagery directly) measuring the anisotropy and the complexity of the seabed. Experimental results suggest that the classification performance of the proposed approach compares favorably to conventional classification algorithms as well as state-of-the-art context-dependent methods. Results also reveal the object features that are salient for performing target classification in different underwater environments.
|Original language||English (US)|
|Number of pages||14|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|State||Published - Oct 2014|
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)