Abstract
Constrained energy minimization (CEM) has been proposed and widely researched in the field of hyperspectral target detection. Generally, it selects one of the target spectra as the representative and then keeps its output constant while minimizing the average filter output energy of the data. However, it has been proven that as the number of bands (L) increases, CEM will gradually lower the average filter output energy when keeping the representative’s output constant. Unavoidably, due to the inherent spatial and temporal variation of the spectra, this will lead to an unreasonable phenomenon, i.e., if L is particularly large, when adding more bands, CEM will suppress more and more pixels, even including the target pixels. This means that the optimal solution of CEM may not correspond to the target detection result that we desire. To deal with this, in this paper, we introduce the third-order statistic (skewness) of the CEM model, served as an auxiliary index to determine whether a band is beneficial to target detection or not. Theoretically, we prove that the skewness index can always exclude the noisy bands with Gaussian distribution. In addition, experiments on several widely used remote sensing data indicate that the index can also efficiently identify informative bands for a better target detection performance.
Highlights
Published: 2 May 2021Target detection is an important research area in the applications of remote sensing, such as climate change [1], camouflage target detection [2], agricultural production [3], land use [4], urban monitoring [5], ship detection [6], etc
For the real data part, movitated by the simple example in Figure 1 and the theoretical analysis in Theorem 2, we mainly focus on investigating the impact of different Ls on the target detection performance, and whether the skewness index can serve as a tool to identify informative bands for a better detection result
Combining the comparison results for the two data set, we conclude that the introduced skewness index can serve as an efficient tool in identifying these highly-correlated bands of the data, and we can use these left informative bands to further improve the performance of target detection
Summary
Target detection is an important research area in the applications of remote sensing, such as climate change [1], camouflage target detection [2], agricultural production [3], land use [4], urban monitoring [5], ship detection [6], etc. With the development of technology, more and more hyperspectral data have become available and have been successfully applied for various applications. Hyperspectral target detection methods can be divided into the following four categories: (1) algorithms based on spectral similarity index, which aim to calculate the spectral difference between target and background pixels. The most commonly used one is the spectral angle mapping [11]; (2) linear mixing model (LMM)-based algorithms, which assume that any mixed pixel can be regarded as a linear combination of endmembers [12]
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