Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection
This addresses target detection in hyperspectral imaging for remote sensing applications, but it is incremental as it builds on existing multiple instance learning and sparse unmixing methods.
The paper tackles the problem of hyperspectral target detection with imprecise labels and sub-pixel mixtures by proposing a multiple instance hybrid estimator that learns discriminative target signatures, achieving superior performance over state-of-the-art algorithms in experiments.
The Multiple Instance Hybrid Estimator for discriminative target characterization from imprecisely labeled hyperspectral data is presented. In many hyperspectral target detection problems, acquiring accurately labeled training data is difficult. Furthermore, each pixel containing target is likely to be a mixture of both target and non-target signatures (i.e., sub-pixel targets), making extracting a pure prototype signature for the target class from the data extremely difficult. The proposed approach addresses these problems by introducing a data mixing model and optimizing the response of the hybrid sub-pixel detector within a multiple instance learning framework. The proposed approach iterates between estimating a set of discriminative target and non-target signatures and solving a sparse unmixing problem. After learning target signatures, a signature based detector can then be applied on test data. Both simulated and real hyperspectral target detection experiments show the proposed algorithm is effective at learning discriminative target signatures and achieves superior performance over state-of-the-art comparison algorithms.