采用 CNN 的高光谱图像图像级分类框架

摘要：高光谱图像比普通图像包含更丰富的光谱和空间信息，广泛应用于军事、农业等领域。随着传感器技术的发展， 高光谱图像的空间分辨率和光谱分辨率得到明显提高，但高光谱图像中可能只包含同一地物的某一部分，且该部分具有 不同的光谱信息，导致传统的像素级高光谱图像分类效果不理想。为此，提出了一种基于卷积神经网络的高光谱图像分 类框架。首先，通过波段选择获取针对小样本高光谱数据的多组假彩色图像；然后引入并行CNN得到不同波段组合的分 类结果；最后采用统计分析策略得到最终的输出结果。实验表明，该方法在同一数据集上的分类精度优于以往算法。

关键词：CNN,高光谱图像,图像分类

期刊：Proceedings of ICSP2018 （IEEE2024）

设备：GaiaField

An Image-Level Classification F ramework for Hyperspectral Image with CNNs

Abstract：Hyperspectral images include richer spectral and spatial information than common images, which are widely used in military, agricultural fields, etc. With the development of sensor technology, the spatial resolution and spectral resolution of hyperspectral images have been improved significantly. However, the disadvantage that there may contain only one part of one object which has different spectral information in hyperspectral images. This will lead to unsatisfactory performance in traditional pixel-level hyperspectral image classification. Thus, a new hyperspectral image classification framework based on convolutional neural network is proposed. First, band selection is adopted to obtain mult iple sets of false color images for small sample hyperspectral data. Then, parallel CNNs are introduced to get the classification results of different band combinations. Finally, statistical analysis strategy is performed to obtain the final output result. Experiments show that the classification accuracy of this method is better than that of the previous algorithm on the same dataset.

Keywords：CNNs, hyperspectral image, image classification