A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Recent developments in hyperspectral sensors have made it possible to acquire hyperspectral images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic subregions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing such as classification, spectral unmixing, dimensionality reduction, band selection, active learning (AL), denoising, and anomaly detection. This article focuses on classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The superpixel creation algorithm framework and postprocessing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this article, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed

Error removal by energy scaling from hyperspectral images for performance improvement of spectral classifiers

In the remote sensing community, HyperSpectral (HS) images (HSI) are becoming increasingly popular as the advancement of technology and the consequent reduction of cost make them financially more accessible. The reason for their success is the higher capability they can offer, with respect to multispectral data, to discriminate classes that are spectrally similar. A series of pre-processing steps such as geometric, radiometric and atmospheric corrections are carried out on raw data captured by HS sensors. The processed data is then passed to the data analyst, whose work generally relies on the assumption that the received HS data is 'clean,' as all possible corrections have already been implemented; however, corrections are hardly perfect and residual disturbances can still bias the quality of results. At this stage, however, all corrections based on ancillary information have already been made and the possibilities for 'exogenous' correction of data are exhausted. More could be possibly done by sourcing additional information from the data itself. In this paper, we propose a simple yet effective additional step for error suppression through energy scaling, termed 'Error Removal by Energy Scaling' (ERES). In classification problems, the absolute value of wavelength lambda is often overlooked, except for, e.g. removal of strong absorption bands; yet the lambda value can actually further support the classification process if their physical meaning is tapped. The proposed ERES method is indeed a non-linear scaling method, derived from physical phenomena linked with radiation extinction properties. In ERES, each band is associated with an energy level, that is inversely related to its own wavelength. The associated nonlinear energy information in HSI, neglected in most classification strategies, prevents optimal separation of class-specific spectral signatures, that are generated by the physics of wave-matter interaction. This is especially true for linear classifiers such as Support Vector Machines (SVM). Removing this physics-linked information makes data more suitable to be classified with physics-unaware classification strategies, typically used in down stream remotely sensed data processing. The relevance of the issue, and the benefits of ERES, are discussed and validated in this work over three different datasets, using accuracy improvements on the popular Spectral Angle Mapper and SVM classifiers as a means to gauge the effectiveness of the correction strategy. Results clearly reveal the positive impact of applying ERES to the data before proceeding to classification