Multi-temporal remote sensing image time-space spectrum integrated fusion method based on coupling sparse tensor decomposition

Abstract

The invention relates to a multi-temporal remote sensing image spatial-temporal spectrum integrated fusion method based on coupling sparse tensor decomposition. The method comprises the following steps: preprocessing a hyperspectral image HtSe-Lsa with high temporal resolution, high spectral resolution and low spatial resolution; and representing the multi-temporal HtSe-Lsa image and the Hsa-LtSe image in a three-dimensional tensor form. The invention has the beneficial effects that a plurality of long-time-sequence images are used for expanding the third dimension, and a plurality of high-resolution images can be generated at the same time through one-time fusion, so that integrated fusion of a plurality of different time-phase images is realized; superposing target images of different time phases in a spectrum dimension by using the multidimensional expression advantage of tensor so as to realize time phase expansion; on one hand, the calculation efficiency is improved through dimensionality reduction, on the other hand, error accumulation caused by too many variables required to be solved due to too high dimensionality is reduced through dimensionality reduction, the complexity of model reconstruction is greatly reduced, and the reconstruction efficiency is improved.

Description

technical field [0001] The invention relates to the field of remote sensing image processing, and in particular includes a multi-temporal remote sensing image time-space-spectrum integration method based on coupled sparse tensor decomposition. Background technique [0002] With the rapid development of remote sensing technology, satellite sensors can obtain a large number of remote sensing images with different temporal, spatial and spectral resolutions every day. Time-series remote sensing images with high spatial and spectral resolution can provide great possibilities for the monitoring and research of rapid surface changes. However, due to the limitation of the satellite sensor imaging system, the time resolution, spatial resolution and spectral resolution of the acquired images are mutually restricted. At present, no satellite sensor in the world can obtain images with high spatial resolution, high spectral resolution and high spectral resolution at the same time. Time-...

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Problems solved by technology

[0004] On the one hand, most of the existing fusion methods are only applied to integrate two kinds of time, space, and spectral relationships, and cannot obtain fused images with high temporal, high spatial, and high spectral resolutions at the same time; on the other hand, these fusion methods Usually it can only be used to generate a single time-phase high-resolution fusion image, but cannot use multiple dense time-series low spatial resolution / spectral resolution images and sparse time-series high spatial resolution / spectral resolution images to generate at one time Multiple High Resolution Images

[0005] Remote sensing images with high temporal, high spatial, and high spectral resolutions are of great significance for fine monitoring of natural resources, precision agriculture and other applications in many fields. However, due to the constraints of sensor imaging systems, a single sensor cannot obtain high spatial and high spectral resolution images

Time-space-spectrum fusion can integrate the complementary advantages of multi-source images to generate high time-space-spectrum resolution images. However, the existing time-space-spectrum fusion methods only output high-space-spectrum Insufficient consideration of the time series relationship between images

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