Deep Learning Patch-Based Approach for Hyperspectral Image Classification-猫眼课题宝

DOI：

10.1109/eit57321.2023.10187387

发表时间：

2023-05

期刊：

2023 IEEE International Conference on Electro Information Technology (eIT)

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通讯作者：

中科院分区：

其他

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作者：

Papia F. Rozario;E. Ruehmann;T. Pham;Tianqi Sun;Jacob Jensen;Hengrui Jia;Zhongyue Yu;Rahul Gomes

高光谱图像的分类是高空间分辨率图像解译的重要步骤。不同的研究表明，空间特征可以为提高高光谱图像分类精度提供补充信息。在本研究中，我们评估了基于卷积自编码器去噪方法的高光谱图像的不同光谱空间分类方法。得到的光谱特征的高维向量通过支持向量机（SVM）、最大似然（ML）和随机森林（RF）等监督算法进行分类。在几张众所周知的高光谱图像上进行的实验表明，基于补丁的3D卷积自编码器在降低数据集中的噪声和保留光谱空间信息方面更有效。随机森林分类器在所有模型中提供最高的分类精度。

Classification of hyperspectral images is an important step of image interpretation from high spatial resolution imagery. Different studies demonstrate that spatial features can provide complementary information for increasing the accuracy of hyperspectral image classification. In this study, we evaluate different methods of spectral-spatial classification of hyperspectral images that are based on denoising methods using convolutional autoencoders. The resulting high-dimensional vectors of spectral features are classified by supervised algorithms such as support vector machine (SVM), maximum likelihood (ML), and random forest (RF). The experiments are performed on several widely known hyperspectral images that reveal a patch-based 3D convolutional autoencoder is more effective in reducing noise in the dataset and retaining spectral-spatial information. Random Forest classifier provides the highest classification accuracy across all the models.

Deep Learning Patch-Based Approach for Hyperspectral Image Classification

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