Abstract
Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R2) of up to 0.979 solves the problem of mixed pixel decomposition.
Subject
General Earth and Planetary Sciences
Reference55 articles.
1. Research into Remote Sensing Technology Application in Geological Hazard Analysis;Huang;J. Guangxi Teach. Educ. Univ. (Nat. Sci. Ed.),2016
2. Advances in researches on hyperspectral remote sensing forestry information-extracting technology;Jian;Spectrosc. Spectr. Anal.,2011
3. Remote sensing of soil properties in precision agriculture: A review
4. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
5. Hyperspectral Remote Sensing Data Analysis and Future Challenges
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献