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Tucker decomposition-based spectral tensor dimension reduction and classification method

A spectrum and dimensionality reduction technology, applied in the field of remote sensing image processing, to achieve the effect of improving classification accuracy and stable results

Active Publication Date: 2017-06-13
NORTHWEST UNIV(CN) +2
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, this method only simply models the hyperspectral cube as a third-order tensor, without considering the real reason that affects the accuracy of hyperspectral classification: the spectral characteristics of ground objects are affected by various factors such as illumination, mixing, atmospheric scattering, and atmospheric radiation.

Method used

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  • Tucker decomposition-based spectral tensor dimension reduction and classification method
  • Tucker decomposition-based spectral tensor dimension reduction and classification method
  • Tucker decomposition-based spectral tensor dimension reduction and classification method

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Embodiment 1

[0042] This embodiment provides a spectral tensor dimensionality reduction method based on Tucker decomposition, which uses randomly selected pixel spectra as intra-class factors, and constructs intra-class factors, class and pixel spectrum bands as a model respectively. A rank 3 tensor, which is subjected to dimensionality reduction based on low-rank tensor decomposition;

[0043] Step 1. In this embodiment, the pixel spectrum in the hyperspectral image of Washington DC Mall of HYDICE is selected, and the original image size is 1280×307. There are a total of 210 bands from 0.4 to 2.4 μm in the visible to infrared spectrum. Due to the opacity of the atmosphere, the bands in the 0.9 to 1.4um region are discarded, and the remaining 191 bands are used as bands of the pixel spectrum. Such as figure 2 As shown, the hyperspectral image includes 7 types of samples, namely: roof (Roof), street (street), lawn (Grass), tree (Tree), path (Path), water body (Water) and shadow (Shadow); ...

Embodiment 2

[0048] In this embodiment, the pixel spectrum of the hyperspectral image selected in Embodiment 1 is used as the training set, and any unclassified pixel spectrum in Washington DC Mall of HYDICE is input as the test pixel spectrum d.

[0049] 其中,d为(0.4012,0.3909,0.3885,0.4026,0.4004,0.3967,0.3778,0.3441,0.3792,0.4121,0.4219,0.4552,0.4969,0.5005,0.501,0.4889,0.4709,0.4665,0.4226,0.4888,0.3852,0.3612,0.3712 ,0.3781,0.3739,0.3624,0.3492,0.3297,0.3159,0.3148,0.3036,0.3096,0.2995,0.2951,0.2817,0.268,0.2558,0.2423,0.2392,0.231,0.2339,0.2303,0.2225,0.2131,0.2022,0.2024,0.206,0.1995 ,0.2006,0.2028,0.1943,0.175,0.1735,0.1809,0.17,0.1621,0.1744,0.1789,0.145,0.1487,0.1724,0.1647,0.1514,0.1302,0.1329,0.4095,0.4073,0.4038,0.3902,0.3184,0.2357,0.1458,0.116 ,0.1377,0.2284,0.2996,0.3431,0.3453,0.3418,0.3389,0.3138,0.2863,0.2684,0.2084,0.0903,0.0497,0.0664,0.132,0.1774,0.186,0.1915,0.2055,0.2131,0.2077,0.1824,0.1871,0.1831,0.1501 ,0.1049,0.0579,0.0128,0.0034,0.0062,0.0155,0.0193,0.0213,0.0441...

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Abstract

The invention discloses a Tucker decomposition-based spectral tensor dimension reduction and classification method. The method comprises the steps of constructing a three-order tensor by taking factors influencing a spectral feature of a ground object as intra-class factors and taking the intra-class factors, classes and pixel spectrums as modes respectively, and performing low-rank tensor decomposition-based dimension reduction on the three-order tensor; performing low-rank tensor decomposition on the three-order tensor to obtain a nuclear tensor Z, a class space matrix Uclass, an intra-class factor space matrix Uwithin-class and a pixel spectral matrix Upixels; and performing classification on unclassified test hyperspectral images d by adopting a supervised classifier. According to the method, the hyperspectral images can be classified after model building, and adjustment does not need to be carried out; for other tensor modeling methods, the best classification effect can be achieved by repeated setting and adjustment of parameters; and all pixel spectrums in a class are mapped to a same coefficient vector, so that the influence of various factors is reduced to minimum, the classification precision is improved, and a result is stable.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a spectral tensor dimension reduction and classification method based on Tucker decomposition. Background technique [0002] Hyperspectral images provide a detailed and rich description of the spectral characteristics of ground objects, greatly improving the classification ability of ground objects, and have been widely used in geological exploration and earth resource investigation, urban remote sensing and planning management, environmental and disaster monitoring, precision agriculture, surveying and mapping and archaeology. [0003] However, hyperspectral images are composed of a large number of band data, and these bands constitute a high-dimensional feature space, and its processing requires a huge amount of calculation, causing a "data disaster". To solve this problem, the most effective way is dimensionality reduction. Principal Compo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2135G06F18/214G06F18/2411
Inventor 彭进业闫荣华汶德胜冯晓毅胡永明王珺
Owner NORTHWEST UNIV(CN)
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