Abstract
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.
Highlights
Accepted: 19 April 2021Remote sensing from aerial and satellite platforms has become increasingly prevalent and is an important source of information in areas of research including disaster relief [1], determining land usage [2] and assessing vegetation health [3]
Any hyperspectral image X can be represented as L individual greyscale images each exposed at a particular wavelength or spectral band λl, X l : l ∈ {1, 2, ..., L}, where L represents the total number of spectral bands
Applying the spatial DR and simplifying the background prior to performing spectral DR improved the performance of all methods but most notably when using Principal Component Analysis (PCA), which was competitive in both datasets with the addition of spatial DR
Summary
Accepted: 19 April 2021Remote sensing from aerial and satellite platforms has become increasingly prevalent and is an important source of information in areas of research including disaster relief [1], determining land usage [2] and assessing vegetation health [3]. Remote sensing data can consist of high resolution RGB colour data, radar, multispectral, or hyperspectral images The latter, while providing a great deal of useful information, often at wavelengths beyond the range of human vision, introduces a vast quantity of data which must be handled and processed. By altering the Near-InfraRed (NIR) band to be placed in the red-edge portion of the spectrum, as is the case when using NDVIre , a much greater separation is achieved (0.09 vs 0.39). As NDVIre provides the best separation between the most difficult targets and the background it is used to implement spatial DR in this paper
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