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An extensive review of hyperspectral image classification and prediction: techniques and challenges

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Abstract

Hyperspectral Image Processing (HSIP) is an essential technique in remote sensing. Currently, extensive research is carried out in hyperspectral image processing, involving many applications, including land cover classification, anomaly detection, plant classification, etc., Hyperspectral image processing is a powerful tool that enables us to capture and analyze an object's spectral information with greater accuracy and precision. Hyperspectral images are made up of hundreds of spectral bands, capturing an immense amount of information about the earth's surface. Accurately classifying and predicting land cover in these images is critical to understanding our planet's ecosystem and the impact of human activities on it. With the advent of deep learning techniques, the process of analyzing hyperspectral images has become more efficient and accurate than ever before. These techniques enable us to categorize land cover and predict Land Use/Land Cover (LULC) with exceptional precision, providing valuable insights into the state of our planet's environment. Image classification is difficult in hyperspectral image processing because of the large number of data samples but with a limited label. By selecting the appropriate bands from the image, we can get the finest classification results and predicted values. To our knowledge, the previous review papers concentrated only on the classification method. Here, we have presented an extensive review of various components of hyperspectral image processing, hyperspectral image analysis, pre-processing of an image, feature extraction and feature selection methods to select the number of features (bands), classification methods, and prediction methods. In addition, we also elaborated on the datasets used for classification, evaluation metrics used, various issues, and challenges. Thus, this review article will benefit new researchers in the hyperspectral image classification domain.

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Data sharing does not apply to this article as no datasets were generated or analyzed during the current study.

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  1. School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore-632014, Tamil Nadu, India

    Ganji Tejasree & Loganathan Agilandeeswari

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Tejasree, G., Agilandeeswari, L. An extensive review of hyperspectral image classification and prediction: techniques and challenges. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18562-9

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