Anomaly Detection System

Abstract

An image analysis system including an image gathering unit that gathers a high-altitude image having multiple channels, an image analysis unit that segments the high-altitude image into a plurality of equally size tiles and determines an index value based on at least one channel of the image where the image analysis unit identifies areas containing anomalies in each image.

Claims

1. An image analysis system including: an image capture unit that captures at least one a high-altitude image having multiple channels; an image analysis unit operating in the memory of a computer that segments the high-altitude image into a plurality of tiles with each tile having a same pixel width and a same pixel height as an adjacent tile and determines an index value based on at least one channel of the image, wherein the image analysis unit performs a pixel by pixel analysis of the pixels in each tile to calculate a mean value for each tile, the image analysis unit applies a Gaussian distribution of the image pixel values to identify outlying values, and the image analysis unit normalizes the image after applying the Gaussian distribution and calculates a mean and standard deviation values of each pixel, and the image analysis unit identifies areas containing anomalies in each image by analyzing the mean value for each area connected to identified anomalies to determine areas where anomalies exist, and a score is assigned to each identified anomaly and an anomaly rectangle is placed on the image to identify areas where anomalies are identified.

2. The image analysis system of claim 1, wherein the index determined is a normal differential vegetation index for a segment of the captured image.

3. The image analysis system of claim 1, wherein the index determined is a soil adjusted vegetation index for a segment of the captured image.

4. The image analysis system of claim 1, wherein the image analysis unit masks the segment of the image using a confidence mask based on the index value.

5. The image analysis system of claim 1, wherein the image analysis unit applies a box averaging threshold to the segment of the normalized image.

6. The image analysis system of claim 5, wherein the image analysis unit calculates a mean for each pixel in the applied box.

7. The image analysis system of claim 6, wherein the image analysis unit removes pixels from the segment of the image that have a calculated mean below a predetermined threshold.

8. The image analysis system of claim 7, wherein the image analysis unit calculates a score for each of the remaining pixels and draws a rectangle around groups of pixels based on the scores of each pixel.

9. An image analysis unit including a processor and a memory with a method of analyzing an image performed in the memory, the method including the steps of: gathering a high-altitude image having multiple channels via an image capture unit; segmenting the high-altitude image into a plurality of tiles with each tile having a same pixel width and a same pixel height as an adjacent tile via an image analysis unit; determining an index value based on at least one channel of the image via the image analysis unit; applying a Gaussian distribution of the image pixel values to identify outlying values, and normalizing the image after applying the Gaussian distribution and calculates a mean and standard deviation values of each pixel, identifying areas containing anomalies in each image via the image analysis unit by analyzing the mean value for each area connected to identified anomalies to determine areas where anomalies exist, and assigning a score to each identified anomaly and placing an anomaly rectangle on the image to identify areas where anomalies are identified.

10. The method of claim 9, wherein the index determined is a normal differential vegetation index for a segment of the captured image.

11. The method of claim 9, wherein the index determined is a soil adjusted vegetation index for a segment of the captured image.

12. The method of claim 9, wherein the step of identifying anomalies includes masking the segment of the image using a confidence mask based on the index value.

13. The method of claim 12, wherein the step of identifying anomalies includes applying a box averaging threshold to the segment of the normalized image.

14. The method of claim 13, wherein the step of identifying anomalies includes calculating a mean for each pixel in the applied box.

15. The method of claim 14, wherein the step of identifying anomalies includes removing pixels from the segment of the image that have a calculated mean below a predetermined threshold.

16. The method of claim 15, wherein the step of identifying anomalies includes calculating a score for each of the remaining pixels and drawing a rectangle around groups of pixels based on the scores of each pixel.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which are incorporated in and constitute a part of this

[0027] specification, illustrate an implementation of the present invention and, together with the description, serve to explain the advantages and principles of the invention. In the drawings:

[0028] FIG. 1 depicts one embodiment of an anomaly identification system consistent with the present invention;

[0029] FIG. 2 depicts one embodiment of an anomaly detection unit;

[0030] FIG. 3 depicts one embodiment of a communication device consistent with the present invention;

[0031] FIG. 4 depicts a schematic representation of a process used to calculate identify anomalies in an image;

DETAILED DESCRIPTION OF THE INVENTION

[0032] Referring now to the drawings which depict different embodiments consistent with the

[0033] present invention, wherever possible, the same reference numbers will be used throughout the drawings and the following description to refer to the same or like parts.

[0034] The anomaly identification system 100 gathers medium to low resolution images gathered from an aircraft flying above 1,500 feet. Each image is then analyzed using NDVI and SDVI parameters and is normalized. After normalization, specific adjacent areas are analyzed to identity anomalies in each portion. After specific anomalies are identified, the system combines the various portions to generate a map of all anomalies it the image that are identified using rectangles outlining the anomaly area.

[0035] FIG. 1 depicts one embodiment of an anomaly identification system 100 consistent with the present invention. The anomaly identification system 100 includes a anomaly identification device 102, a communication device 1 104, a communication device 2 106 each communicatively connected via a network 108. The anomaly identification system 100 further includes an image gathering unit 110, an image analysis unit 112, a preprocessing unit 114 and an anomaly identification unit 116.

[0036] The image gathering unit 110 and image analysis unit 112 may be embodied by one or more servers. Alternatively, each of the preprocessing unit 114 and anomaly identification unit 116 may be implemented using any combination of hardware and software, whether as incorporated in a single device or as a functionally distributed across multiple platforms and devices.

[0037] In one embodiment, the network 108 is a cellular network, a TCP/IP network, or any other suitable network topology. In another embodiment, the anomaly identification device may be servers, workstations, network appliances or any other suitable data storage devices. In another embodiment, the communication devices 104 and 106 may be any combination of cellular phones, telephones, personal data assistants, or any other suitable communication devices. In one embodiment, the network 102 may be any private or public communication network known to one skilled in the art such as a local area network (LAN), wide area network (WAN), peer-to-peer network, cellular network or any suitable network, using standard communication protocols. The network 108 may include hardwired as well as wireless branches. The image gathering unit 112 may be a digital camera.

[0038] FIG. 2 depicts one embodiment of an anomaly detection device 102. The anomaly detection device 102 includes a network I/O device 204, a processor 202, a display 206 and a secondary storage 208 running image storage unit 210 and a memory 212 running a graphical user interface 214. The image gathering unit 112, operating in memory 208 of the anomaly detection unit 102, is operatively configured to receive an image from the network I/O device 204. In one embodiment, the processor 202 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a microprocessor or any other suitable processing device. The memory 212 may include a hard disk, random access memory, cache, removable media drive, mass storage or configuration suitable as storage for data, instructions, and information. In one embodiment, the memory 208 and processor 202 may be integrated. The memory may use any type of volatile or non-volatile storage techniques and mediums. The network I/O line 204 device may be a network interface card, a cellular interface card, a plain old telephone service (POTS) interface card, an ASCII interface card, or any other suitable network interface device. The anomaly detection unit 114 may be a compiled program running on a server, a process running on a microprocessor or any other suitable port control software.

[0039] FIG. 3 depicts one embodiment of a communication device 104/106 consistent with the present invention. The communication device 104/1106 includes a processor 302, a network I/O Unit 304, an image capture unit 306, a secondary storage unit 308 including an image storage device 310, and memory 312 running a graphical user interface 314. In one embodiment, the processor 302 may be a central processing unit (CPU), a application specific integrated circuit (ASIC), a microprocessor or any other suitable processing device. The memory 312 may include a hard disk, random access memory, cache, removable media drive, mass storage or configuration suitable as storage for data, instructions, and information. In one embodiment, the memory 312 and processor 302 may be integrated. The memory may use any type of volatile or non-volatile storage techniques and mediums. The network I/O device 304 may be a network interface card, a plain old telephone service (POTS) interface card, an ASCII interface card, or any other suitable network interface device.

[0040] In one embodiment, the network 108 may be any private or public communication network known to one skilled in the art such as a Local Area Network (LAN), Wide Area Network (WAN), Peer-to-Peer Network, Cellular network or any suitable network, using standard communication protocols. The network 108 may include hardwired as well as wireless branches.

[0041] FIG. 4 depicts a schematic representation of a process used to identify anomalies in an image. In step 402, an image is captured by the image gathering unit 112. The image may be captured using any conventional methods of capturing a digital image. In one embodiment, the image is a high resolution raw image. In one embodiment, the image is captured from an aircraft flying at least 1,500 feet above the surface. In step 404, the Normal Differential Vegetation Index (NDVI) or the Soil Adjusted Vegetation Index. The NDVI is calculated using the following equation:

[00001]$\mathrm{NDVI}=\frac{\mathrm{NI}R-\mathrm{RED}}{\mathrm{NI}R+\mathrm{RED}}$

[0042] The near field channel (NIR) is identified in the image by the image analysis unit 114 where the near field channel is between 800 nm and 850 nm. The red channel (RED) is identified in each image by the image analysis unit 114 where the red channel is between 650 nm and 680 nm. The SDVI is determined using the following equation:

[00002]$\mathrm{SAVI}=\frac{\mathrm{NIR}-\mathrm{RED}}{\left(\mathrm{NIR}+\mathrm{RED}+L\right)\left(1+L\right)}$ [0043] Where L=0.5.

[0044] In step 406, the image analysis unit 114 masks the image using a confidence mask based on the NDVI or SDVI calculation. In step 408, the image is normalized. In one embodiment, a Gaussian distribution of image pixel values is performed by the image analysis unit 114. After the Gaussian distribution is applied, outlying pixels are identified as potential anomalies. The image is normalized using the following equation:

[00003]$\mathrm{normalized}\left(x,y\right)=\frac{\mathrm{image}\left(x,y\right)-\left(x,y\right)}{\left(x,y\right)}$ [0045] where x is a row of pixels, y is a column of pixels, and (x,y) and (x,y) are the mean and standard deviation values. In step 410, the image analysis unit 114 calculates the mean and standard deviation values based on the portion of the image being analyzed. For global images, the mean and standard deviation are calculated taking into account all pixels in the image. For local images, i.e. the pixel (x,y), the mean and the standard deviation are determined taking into account only the square area around the pixel where the square area is a constant predetermined size.

[0046] In step 412, the image analysis unit 114 applies a box averaging threshold to prospective anomalies identified in the image. In one embodiment, a square box of a predetermined size is positioned around a specific anomaly area. In one embodiment, the box is 50 pixels by 50 pixels. The pixels in the box are scanned one pixel at a time and the mean value of the pixels in the box is calculated. If the mean value is greater than a predetermined threshold, the box is marked as an anomaly. In step 414, small areas marked as anomalies, areas less than 0.1% of the entire image, are removed as marked anomalies. In step 416, areas connected to the identified anomaly areas to determine the portions in each area that represent an anomaly. In step 418, cover rectangles are positioned around each anomaly. In step 420, the image analysis unit 114 calculates a score for each identified anomaly. In step 422, the anomaly rectangles are overlaid on the image as a whole to identify the anomaly areas.