Background suppression for anomaly detection

Patent metadata

Field	Value
Publication number	US-11417090-B2
Application number	US-202016790242-A
Country	US
Kind code	B2
Filing date	Feb 13, 2020
Priority date	Feb 18, 2019
Publication date	Aug 16, 2022
Grant date	Aug 16, 2022

Abstract

Systems and methods for anomaly detection are provided. The method includes structuring a multi-channel spatial-temporal sequence as a four-dimensional array. The method also includes decomposing the four-dimensional array to form a low-rank component representing a background signal and a residual component representing anomalies for each time point of the multi-channel spatial-temporal sequence. The method further includes determining a sequence of anomaly maps by stacking the residual components at all time points together. Anomalies are identified based on the sequence of anomaly maps.

First claim

What is claimed is:

1. A method for anomaly detection, comprising:
structuring a multi-channel spatial-temporal sequence as a four-dimensional array;
decomposing the four-dimensional array to form a low-rank component representing a background signal and a residual component representing anomalies for each time point of the multi-channel spatial-temporal sequence;
determining a sequence of anomaly maps by stacking the residual components at all time points together; and
identifying anomalies based on the sequence of anomaly maps.

2. The method as recited in claim 1 , wherein identifying the anomalies based on the sequence of anomaly maps further comprises:
identifying the anomalies in a hyperspectral imaging sequence.

3. The method as recited in claim 1 , further comprising:
implementing flattening to account for dependencies between different spatial locations and different channels.

4. The method as recited in claim 1 , wherein decomposing the four-dimensional array further comprises:
decomposing an input signal array X into two components: a low-rank component L and a residual component R,
wherein X=L+R, and
wherein L represents a projection of an original signal onto a background subspace, and R represents a background-suppressed signal of an anomaly target.

5. The method as recited in claim 1 , wherein decomposing the four-dimensional array further comprises:
assigning positive integer values that represent an estimated degree of variation of the background signal for each dimension.

6. The method as recited in claim 1 , wherein decomposing the four-dimensional array further comprises:
determining a set of basis that characterizes a variation of an input signal in each dimension; and
condensing an original signal into a coefficient representation with respect to the set of basis.

7. The method as recited in claim 6 , further comprising:
reconstructing an array including the input signal from a projection of an original signal onto a background subspace, wherein the reconstructed array is limited to information on the set of basis.

8. The method as recited in claim 1 , wherein structuring the multi-channel spatial-temporal sequence further comprises:
structuring a hyperspectral imaging sequence with two-dimensional spatial scenes.

9. The method as recited in claim 8 , wherein structuring the multi-channel spatial-temporal sequence further comprises:
implementing monitoring in agriculture and environment based on detecting at least one of nutrition, water deficiency of crops, and gas pipeline leakage.

10. The method as recited in claim 1 , further comprising:
determining at least one of daily or seasonal temperature variations and an inter-dependency between spatial locations and spectral bands.

11. The method as recited in claim 1 , wherein the four-dimensional array includes dimensions of distance, angle, frequency channel and time.

12. A computer system for anomaly detection, comprising:
a processor device operatively coupled to a memory device, the processor device being configured to:
structure a multi-channel spatial-temporal sequence as a four-dimensional array;
decompose the four-dimensional array to form a low-rank component representing a background signal and a residual component representing anomalies for each time point of the multi-channel spatial-temporal sequence;
determine a sequence of anomaly maps by stacking the residual components at all time points together; and
identify anomalies based on the sequence of anomaly maps.

13. The system as recited in claim 12 , wherein, when identifying the anomalies based on the sequence of anomaly maps, the processor device is further configured to:
identify the anomalies in a hyperspectral imaging sequence.

14. The system as recited in claim 12 , wherein the processor device is further configured to:
implement flattening to account for dependencies between different spatial locations and different channels.

15. The system as recited in claim 12 , wherein, when decomposing the four-dimensional array, the processor device is further configured to:
decompose an input signal array X into two components: a low-rank component L and a residual component R,
wherein X=L+R, and
wherein L represents a projection of an original signal onto a background subspace, and R represents a background-suppressed signal of an anomaly target.

16. The system as recited in claim 12 , wherein, when decomposing the four-dimensional array, the processor device is further configured to:
assign positive integer values that represent an estimated degree of variation of the background signal for each dimension.

17. The system as recited in claim 12 , wherein, when decomposing the four-dimensional array, the processor device is further configured to:
determine a set of basis that characterizes a variation of an input signal in each dimension; and
condense an original signal into a coefficient representation with respect to the set of basis.

18. The system as recited in claim 17 , wherein the processor device is further configured to:
reconstruct an array including the input signal from a projection of an original signal onto a background subspace, wherein the reconstructed array is limited to information on the set of basis.

19. The system as recited in claim 11 , wherein, when applying the NMT model to quantify strength of invariant relationship, the processor device is further configured to:
determining at least one of daily or seasonal temperature variations and an inter-dependency between spatial locations and spectral bands.

Assignees

• Nec Lab America Inc
• Nec Corp

Inventors

• Chen Yuncong
• Song Dongjin
• Chen Haifeng

Classifications

• G06V10/22

  by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition · CPC title

• G06V10/143

  Sensing or illuminating at different wavelengths · CPC title

• G06V20/188 Primary

  Vegetation · CPC title

• G06V20/194

  using hyperspectral data, i.e. more or other wavelengths than RGB · CPC title