Multispectral Transforms Using Convolution Neural Networks for Remote Sensing Multispectral Image Compression

Abstract

A multispectral image is a three-order tensor since it is a three-dimensional matrix, i.e.one spectral dimension and two spatial position dimensions. Multispectral image compression canbe achieved by means of the advantages of tensor decomposition (TD), such as NonnegativeTucker Decomposition (NTD). Unfortunately, the TD suffers from high calculation complexity andcannot be used in the on-board low-complexity case (e.g., multispectral cameras) that the hardwareresources and power are limited. Here, we propose a low-complexity compression approach formultispectral images based on convolution neural networks (CNNs) with NTD. We construct anew spectral transform using CNNs, where the CNNs are able to transform the three-dimensionspectral tensor from large-scale to a small-scale version. The NTD resources only allocate thesmall-scale three-dimension tensor to improve calculation efficiency. We obtain the optimizedsmall-scale spectral tensor by the minimization of original and reconstructed three-dimensionspectral tensor in self-learning CNNs. Then, the NTD is applied to the optimized three-dimensionspectral tensor in the DCT domain to obtain the high compression performance. We experimentallyconfirmed the proposed method on multispectral images. Compared to the case that the newspectral tensor transform with CNNs is not applied to the original three-dimension spectral tensorat the same compression bit-rates, the reconstructed image quality could be improved. Comparedwith the full NTD-based method, the computation efficiency was obviously improved with only asmall sacrifices of PSNR without affecting the quality of images.

A New Diffuse Optics Emitter for High Visual Diffuse Transmission Density Measurement

“…A new diffuse optics emitter is described in this paper, and it is proven to be a high diffuse and flux light source in the visual spectrum. It can be used in many diffuse optics systems, for highly precision measurement, image project, image analysis, or other usages [32][33][34][35].…”

Spectral Imagery Tensor Decomposition for Semantic Segmentation of Remote Sensing Data through Fully Convolutional Networks

“…Recently, three works close to our research [27][28][29] were published. In [27] An et al proposed an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral image dimensionality reduction, and Li et al in [28] proposed a low-complexity compression approach for multispectral images based on convolution neural networks CNNs with nonnegative Tucker decomposition (NTD). Nevertheless, these methods reduce the tensor in every dimension, which is self-defeating for a segmentation CNN.…”

“…Nevertheless, these methods reduce the tensor in every dimension, which is self-defeating for a segmentation CNN. Besides, the non-negative decomposed tensor proposed in [28] causes slower convergence in DL algorithms. In [29] An et al proposed a tensor discriminant analysis (TDA) model via compact feature representation, wherein the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more discriminant.…”

Comprehensive review of hyperspectral image compression algorithms

“…Decomposed tensor is then encoded and transmitted through the channel. Some state-of-the-art algorithms of the techniques non-negative tucker decomposition (NTD) 8 -DWT, convolution neural network (CNN) 31 -NTD, and NTD-DCT 12 have shown excellent results.…”

“…Both spatial and spectral domains are compressed separately using TD. Tensor decomposition has also been used along with deep learning technique in CNN-NTD, 31 where CNN-based transform is proposed to transform large-scale spectral tensor into small-scale. Then, NTD is applied to further reduce the dimensionality of small scale tensor obtained in first step.…”