Abstract
Compared with traditional optical and multispectral remote sensing images, hyperspectral images have hundreds of bands that can provide the possibility of fine classification of the earth’s surface. At the same time, a hyperspectral image is an image that coexists with the spatial and spectral. It has become a hot research topic to combine the spatial spectrum information of the image to classify hyperspectral features. Based on the idea of spatial–spectral classification, this paper proposes a novel hyperspectral image classification method based on a segment forest (SF). Firstly, the first principal component of the image was extracted by the process of principal component analysis (PCA) data dimension reduction, and the data constructed the segment forest after dimension reduction to extract the non-local prior spatial information of the image. Secondly, the images’ initial classification results and probability distribution were obtained using support vector machine (SVM), and the spectral information of the images was extracted. Finally, the segment forest constructed above is used to optimize the initial classification results and obtain the final classification results. In this paper, three domestic and foreign public data sets were selected to verify the segment forest classification. SF effectively improved the classification accuracy of SVM, and the overall accuracy of Salinas was enhanced by 11.16%, WHU-Hi-HongHu by 15.89%, and XiongAn by 19.56%. Then, it was compared with six decision-level improved space spectrum classification methods, including guided filtering (GF), Markov random field (MRF), random walk (RW), minimum spanning tree (MST), MST+, and segment tree (ST). The results show that the segment forest-based hyperspectral image classification improves accuracy and efficiency compared with other algorithms, proving the algorithm’s effectiveness.
Highlights
Remote-sensing research focusing on image classification has long attracted the attention of the remote-sensing community because classification results are the basis for many domanial applications [4]
There is a significant amount of spectral redundancy that exists in Hyperspectral image (HSI) data, so some level of signal compression or dimension reduction is appropriate [31]
A represents the number of vertices in the tree with the fewest vertices in the theof partition forest.The γ represents represents thethe influence between vertices in calculating calculating the probability aggregation
Summary
Spatial–spectral hyperspectral image classification methods are categorized into feature-level fusion, decision-level fusion, and deep learning. While the traditional methods encounter bottlenecks due to limited data fitting and representation capabilities, the deep learning method obtains good classification results due to the extraction of high-frequency information (including spatial information) of images, such as CNN [21]. The feature of the method is that a small amount of training data can be used to integrate image spectral information and spatial information, and the method has achieved a good classification effect. The spatial information of the HSI image was used to construct the segment forest, and the spectral information of the HSI image was combined to improve the classification accuracy and calculation efficiency; Based on the segment tree method, the merging and filtering of trees are improved.
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