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Hyperspectral image super-resolution employing nonlocal block and hybrid multiscale three-dimensional convolution
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2022-09-01 , DOI: 10.1117/1.jrs.16.036518
Cong Liu 1 , Dan Liu 1
Affiliation  

Due to the special characteristics of hyperspectral images (HSIs), hundreds of continuous bands, and low spatial resolution, it is of great importance to explore the coherence among hyperspectral bands and extract the spatial information as far as possible to reconstruct the high-resolution (HR) HSIs, in which most of the methods failed. We propose an HSI super-resolution (SR) method termed NLB-HMS3D, which consists of two main parts named the spatial–similarity features module and the spatial and spectral correlation utilization module. Different from the majority of existing methods that stack multiple parallel two-dimensional convolution layers to blindly extract more spatial features, we introduce the nonlocal block to expand the receptive fields to thoroughly dig the spatial–similarity features from the image itself. This block not only greatly improves the effectiveness but also reduces tons of parameters. To better preserve the spectral details, we further propose a new block called multiscale spectral features fusion block using the separated three-dimensional convolution with different convolution kernel sizes to explore the diverse spatial–spectral features and fuse them to recover better spectral details. The experiments and data analysis demonstrate that NLB-MS3D can obtain superior performance over many existing state-of-the-art algorithms.

中文翻译:

采用非局部块和混合多尺度三维卷积的高光谱图像超分辨率

由于高光谱图像(HSI)具有数百个连续波段和空间分辨率低的特殊特性,探索高光谱波段之间的相干性并尽可能提取空间信息以重建高分辨率具有重要意义。 HR)HSI,其中大多数方法都失败了。我们提出了一种称为 NLB-HMS3D 的 HSI 超分辨率 (SR) 方法,该方法由两个主要部分组成,称为空间相似性特征模块和空间和光谱相关性利用模块。与现有的大多数堆叠多个并行二维卷积层以盲目提取更多空间特征的方法不同,我们引入了非局部块来扩展感受野,从而彻底挖掘图像本身的空间相似性特征。这个块不仅大大提高了效率,而且减少了大量的参数。为了更好地保留光谱细节,我们进一步提出了一个称为多尺度光谱特征融合块的新模块,它使用具有不同卷积核大小的分离三维卷积来探索不同的空间光谱特征并将它们融合以恢复更好的光谱细节。实验和数据分析表明,NLB-MS3D 可以获得优于许多现有最先进算法的性能。我们进一步提出了一个新的块,称为多尺度光谱特征融合块,使用具有不同卷积核大小的分离的三维卷积来探索不同的空间光谱特征并将它们融合以恢复更好的光谱细节。实验和数据分析表明,NLB-MS3D 可以获得优于许多现有最先进算法的性能。我们进一步提出了一个新的块,称为多尺度光谱特征融合块,使用具有不同卷积核大小的分离的三维卷积来探索不同的空间光谱特征并将它们融合以恢复更好的光谱细节。实验和数据分析表明,NLB-MS3D 可以获得优于许多现有最先进算法的性能。
更新日期:2022-09-01
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