IEEE Geoscience and Remote Sensing Magazine· 2013Q1· Review
Hyperspectral Remote Sensing Data Analysis and Future Challenges
- 2,105citations
- Q1SCImago
- 2013year
Short summary
Hyperspectral remote sensing offers high-resolution Earth surface data for material identification and parameter estimation, but faces challenges from high dimensionality, spectral mixing, and noise.
AI-generated from the title and abstract; the full text is not read.
Key points
- Hyperspectral sensors provide unprecedented spectral, spatial, and temporal resolution for Earth surface analysis.
- Key challenges include high data dimensionality, spectral mixing (linear/nonlinear), and noise/atmospheric effects.
- The paper reviews six core analysis methods: data fusion, unmixing, classification, target detection, parameter retrieval, and fast computing.
- It outlines the state-of-the-art for these methods and identifies future research challenges.
AI-generated from the title and abstract; the full text is not read.
Abstract
Hyperspectral remote sensing technology has advanced significantly in the past two decades. Current sensors onboard airborne and spaceborne platforms cover large areas of the Earth surface with unprecedented spectral, spatial, and temporal resolutions. These characteristics enable a myriad of applications requiring fine identification of materials or estimation of physical parameters. Very often, these applications rely on sophisticated and complex data analysis methods. The sources of difficulties are, namely, the high dimensionality and size of the hyperspectral data, the spectral mixing (linear and nonlinear), and the degradation mechanisms associated to the measurement process such as noise and atmospheric effects. This paper presents a tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing. In all topics, we describe the state-of-the-art, provide illustrative examples, and point to future challenges and research directions.
The authors' abstract, as published at the source. IEEE Geoscience and Remote Sensing Magazine, 2013 · DOI ↗
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Field: Media Technology
Media TechnologyEngineering