Multispectral image compression can considerably reduce the volume of data and promote their application. However, conventional single-scale compression schemes, such as JPEG2000 and three-dimensional set partitioning in hierarchical tree (3D-SPIHT), do not accurately preserve the features of images due to the complex features of multispectral images. A compression framework based on adaptive multiscale feature extraction with a convolutional neural network is proposed. First, an adaptive multiscale feature extraction module, which is the basic component of the compression framework, is designed to extract the multiscale spatial–spectral features of the multispectral images and adaptively adjust the weights of the features according to the content of the images. Second, the encoder, which is composed of multiscale feature extraction modules, extracts the multiscale spatial–spectral features of the multispectral images, and the extracted features are quantized and encoded by the quantizer and the entropy coder to generate a compressed bitstream. Third, the decoder, which is structurally similar to the encoder, is utilized to recover the images. The rate-distortion optimizer is embedded in the encoder to control the trade-off between the rate loss and the distortion. The results of these experiments on multispectral images of the Landsat 8 satellite and the WorldView-3 satellite validate the better performance of our compression framework compared with the performances of conventional schemes, including JPEG2000 and 3D-SPIHT. In order to further verify the effectiveness of multiscale features, the framework is compared with a single-scale compression algorithm based on deep learning, the experimental results validate that the performance of the single-scale compression algorithm superior to the conventional schemes but inferior to our multiscale algorithm, which indicates that the multiscale features can significantly improve the performance of the compression algorithm.

中文翻译：

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基于自适应多尺度特征提取的端到端多光谱图像压缩框架

多光谱图像压缩可以大大减少数据量并促进其应用。然而，由于多光谱图像的复杂特征，诸如JPEG2000和分层树中的三维集划分（3D-SPIHT）的常规单尺度压缩方案不能准确地保留图像的特征。提出了一种基于卷积神经网络的自适应多尺度特征提取的压缩框架。首先，自适应多尺度特征提取模块是压缩框架的基本组件，旨在提取多光谱图像的多尺度空间光谱特征，并根据图像的内容自适应地调整特征的权重。其次，编码器由多尺度特征提取模块组成，提取多光谱图像的多尺度空间光谱特征，然后通过量化器和熵编码器对提取的特征进行量化和编码，以生成压缩比特流。第三，在结构上类似于编码器的解码器被用来恢复图像。速率失真优化器嵌入在编码器中，以控制速率损失和失真之间的权衡。这些对Landsat 8卫星和WorldView-3卫星的多光谱图像进行的实验结果证明，与包括JPEG2000和3D-SPIHT的传统方案相比，我们的压缩框架具有更好的性能。为了进一步验证多尺度特征的有效性，将该框架与基于深度学习的单尺度压缩算法进行了比较，