review · Geo-spatial Information Science
Hyperspectral image classification is a vital capability in modern remote sensing, yet improving its speed and accuracy remains challenging due to inherent constraints in hyperspectral data. This review examines how these imaging techniques are applied to land use and land cover mapping. The work systematically outlines the primary challenges in the field, catalogues and assesses relevant hyperspectral datasets, and evaluates both conventional machine learning algorithms and advanced approaches such as deep learning and spectral decomposition. Finally, it outlines anticipated development trajectories and future technical hurdles. By synthesizing these elements, the review establishes a foundational resource for navigating the current state and emerging directions of hyperspectral classification for land monitoring.
Accurate land use and land cover mapping is crucial for environmental monitoring, urban planning, and resource management. Hyperspectral sensors capture detailed surface data, but making sense of this information requires complex analytical models. Clarifying current methods and datasets helps practitioners choose the right analytical tools to assess land changes effectively.
The work serves as early-stage research infrastructure, surveying algorithmic methods and data repositories relevant to developers of geospatial analytics and remote-sensing software. While it identifies deep learning and spectral decomposition as promising techniques for land mapping, the abstract does not indicate a specific commercial application pathway or prototype ready for deployment.
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In the rapidly evolving realm of remote sensing technology, the classification of Hyperspectral Images (HSIs) is a pivotal yet formidable task. Hindered by inherent limitations in hyperspectral imaging, enhancing the accuracy and efficiency of HSI classification remains a critical and much-debated issue. This review study focuses on a key application area in HSI classification: Land Use/Land Cover (LULC). Our study unfolds in fourfold approaches. First, we present a systematic review of LULC hyperspectral image classification, delving into its background and key challenges. Second, we compile and analyze a number of datasets specific to LULC hyperspectral classification, offering a valuable resource. Third, we explore traditional machine learning models and cutting-edge methods in this field, with a particular focus on deep learning, and spectral decomposition techniques. Finally, we comprehensively analyze future developmental trajectories in HSI classification, pinpointing potential research challenges. This review aspires to be a cornerstone resource, enlightening researchers about the current landscape and future prospects of hyperspectral image classification.
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DOI: 10.1080/10095020.2024.2332638
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