# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 1 Introduction  
Hyperspectral imaging (HSI) is an established and recognized technique in numerous applications, such as agriculture and forestry [1], the food industry [2], humanitarian demining [3, 4], medicine [5], search and rescue missions [6, 7], and water resources [8]. Hyperspectral sensors are passive instruments that record reflected electromagnetic radiation in tens or hundreds of narrow and consecutive spectral bands. In remote sensing applications, the primary radiation source in visible, near-infrared, and shortwave infrared electromagnetic spectrum regions is the Sun. In contrast, in other applications (i.e., food science or medicine), artificial radiation sources can be used. HSI relies on the fact that every material possesses a unique spectral signature. A spectral signature or spectral reflectance refers to unique reflectance (ratio of reflected and incident electromagnetic radiation) variation as a function of wavelength. The ability to detect or identify materials based on their spectral signatures resulted in the development of two leading processing chains of hyperspectral images: classification and target detection (TD). Classification arranges pixels in spectrally disjoint thematic classes. TD aims to find predefined objects or materials in the image or solve the binary problem of whether the pixel under test (PUT) belongs to some general pattern of the image or deviates from it. In other words, TD needs to determine whether the PUT belongs to the background or is a target. Even though TD can be treated as a binary classificationproblem, at least on the theoretical scale, there are compelling reasons why these two processing chains should be separated. For something to be considered a target, it should cover only a negligible image area. An image would then consist of a background class that would contain all or almost all image pixels and a scarcely populated or empty target class. An optimization criterion that minimizes classification error would lead to classifying all pixels as a background and result in missed detections, simultaneously achieving very high classification accuracy. More detailed discussion on this topic can be found in [9]. When TD algorithms are used to discover predetermined materials or objects, they are called spectral matching detection algorithms [10] or spectral signature-based target detectors [11]. These algorithms can be regarded as examples of supervised learning or supervised TD, as they require a-priori knowledge of target spectral signature. Spectral matching detection algorithms assess the similarity between the PUT and the known spectral signature. Required spectral signatures can be obtained from a spectral library, by a field measurement with a spectroradiometer, or by identification of the target pixel in the hyperspectral image. If spectral signatures are acquired from a spectral library or by a spectroradiometer, they are usually expressed in reflectance units. Although TD in hyperspectral images relies on spectral signatures that consist of dimensionless reflectance values, hyperspectral sensors originally measure spectral radiance instead of reflectance. Spectral radiance is a radiant flux in a given direction per unit projected area per unit solid angle as a function of wavelength [12]. Illumination conditions, sensor characteristics, and atmospheric transmission are the leading causes of the differences between reflectance and radiance spectrum. Conversion of at-sensor radiance to ground pixel reflectance spectrum requires radiometric calibration that consists of sensor calibration and atmospheric, solar, and topographic correction [13]. This step is essential, as it dramatically influences detection results. Radiometric calibration is unnecessary if a target pixel can be identified in the hyperspectral image, but this case is usually unfeasible in real-life applications.