Abstract:
The binary hypothesis testing (BHT) is one of the most important models in hyperspectral target detection (HTD). However, this model is generally based on a linear mixtur...Show MoreMetadata
Abstract:
The binary hypothesis testing (BHT) is one of the most important models in hyperspectral target detection (HTD). However, this model is generally based on a linear mixture model (LMM) and might be inaccurate to reflect target and background characterizations in some scenes. This article presents a bilinear sparse target detector (BSTD) by applying the bilinear sparse mixture model (BSMM) to a popular BHT-based detection algorithm termed adaptive matched subspace detector (AMSD), which takes bilinear target–background interaction and sparse abundance into account. Moreover, as AMSD relies heavily on background subspace, we design a robust background subspace construction method. Specifically, we first classify each pixel into noise, border, or other particular instances according to its density, which is measured by jointly spatial–spectral distance. With the coarse classification map, a class-guided automatic background generation (CABG) process is introduced to reliably generate pure background samples. Detection statistics and component analysis on five real-world hyperspectral images verify the effectiveness of our BSTD method.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 60)
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- IEEE Keywords
- Index Terms
- Binary Hypothesis ,
- Hyperspectral Target Detection ,
- Generation Process ,
- Mixture Model ,
- Classification Maps ,
- Sparse Model ,
- Automatic Generation ,
- Bilinear Model ,
- Adaptive Detection ,
- Pure Background ,
- Binary Hypothesis Testing ,
- Linear Mixture Model ,
- Coarse Classification ,
- Receiver Operating Characteristic Curve ,
- Detection Performance ,
- Receiver Operating Characteristic Analysis ,
- Spectral Resolution ,
- Linear Coefficient ,
- Subjective Evaluation ,
- Objective Evaluation ,
- Target Spectrum ,
- Airborne Visible/Infrared Imaging Spectrometer ,
- False Alarm Rate ,
- Boundary Pixels ,
- Cluster Centers ,
- Noisy Pixels ,
- Background Suppression ,
- Generalized Likelihood Ratio Test ,
- Orthogonal Subspace ,
- Endmembers
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Binary Hypothesis ,
- Hyperspectral Target Detection ,
- Generation Process ,
- Mixture Model ,
- Classification Maps ,
- Sparse Model ,
- Automatic Generation ,
- Bilinear Model ,
- Adaptive Detection ,
- Pure Background ,
- Binary Hypothesis Testing ,
- Linear Mixture Model ,
- Coarse Classification ,
- Receiver Operating Characteristic Curve ,
- Detection Performance ,
- Receiver Operating Characteristic Analysis ,
- Spectral Resolution ,
- Linear Coefficient ,
- Subjective Evaluation ,
- Objective Evaluation ,
- Target Spectrum ,
- Airborne Visible/Infrared Imaging Spectrometer ,
- False Alarm Rate ,
- Boundary Pixels ,
- Cluster Centers ,
- Noisy Pixels ,
- Background Suppression ,
- Generalized Likelihood Ratio Test ,
- Orthogonal Subspace ,
- Endmembers
- Author Keywords