Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications
Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications
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DOI：
10.3390/rs13091647
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发表时间：
2021-04
期刊：
影响因子：
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通讯作者：
Fraser Macfarlane;P. Murray;S. Marshall;Henry White
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文献类型：
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作者：
Fraser Macfarlane;P. Murray;S. Marshall;Henry White
目标检测与分类是高光谱成像在遥感领域的重要应用。在过去的几十年里，已经发展了许多用于高光谱图像中的目标检测的算法。由于高光谱图像的性质，它们显示出大量的冗余信息，因此是可压缩的。降维是数据压缩和去噪的有效手段。虽然光谱降维在高光谱目标检测应用中很普遍，但很少利用场景的空间冗余。通过应用简单的空间掩蔽技术作为预处理步骤来忽略明确不感兴趣的像素，后续的光谱降维过程更简单、成本更低且信息量更大。本文提出了一种在将目标检测算法应用于合成场景之前对高光谱图像进行空间和光谱压缩的处理流水线。在所提出的流水线中，评估了几种不同的光谱降维方法和目标检测算法的组合。我们发现，与未经处理的数据相比，自适应余弦估计产生了更好的F1分数和马修斯相关系数。实验还表明，在保持目标检测性能的前提下，使用该流水线可以将数据压缩90%以上。
Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.