# US 2021/0241014 A1 · Hyperspectral target identification > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241014 A1 - Document ID: US20210241014A1 - Status: Published application - Kind: A1 - Application number: 16/782,108 - Filed: 2020-02-05 - Published: 2021-08-05 - Earliest claimed: 2020-02-05 - Assignees: BAE Systems Information and Electronic Systems Integration Inc. - Applicants: BAE SYSTEMS Information and Electronic Systems Integration Inc. - Inventors: Michael J. CHOINIERE - Web page: https://patentgrep.com/patent/US20210241014A1 ## CPC - G06V 10/143: Sensing or illuminating at different wavelengths - G06F 18/2113: by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation - G06F 18/2431: Multiple classes - G06F 18/25: Fusion techniques - G06V 10/50: by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis - G06V 10/764: using classification, e.g. of video objects - G06V 10/80: Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level (multimodal speaker identification or verification G10L17/10) - G06V 2201/07: Target detection - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract The system and method for hyperspectral target identification provides for sue of a sensor array such that for a 2 by 2 pixel, each pixel is set to a different wavelength by employing a band pass filter, so that one can determine whether a cluster set is of a natural object or a man-made object. Using ratios of the collected energy within the partitioned sub-bands one can make near real-time declarations about targets and in some cases, friend or foe determinations. ## Claims ### Claim 1 A method for hyperspectral target identification, comprising: mapping specific wavelengths and bandwidths to each of a plurality of pixels in a cluster within a sensor array using a series optical filters for the specific clutter signatures; calculating ratios between pixel pairs from the plurality of pixels on the sensor array, wherein at least four pixels are used for calculating ratios and one of the four pixels is used as a reference pixel; and eliminating clutter from an image comprising the plurality of pixels on the sensor array based on the calculated ratios using a programmable logic device having contrast spatial filters. ### Claim 2 (depends on 1) The method for hyperspectral target identification according to claim 1, wherein the programmable logic device is an FPGA. ### Claim 3 (depends on 1) The method for hyperspectral target identification according to claim 1, wherein the elimination of clutter includes the removal of vegetation from the image. ### Claim 4 (depends on 1) The method for hyperspectral target identification according to claim 1, wherein the elimination of clutter from the image further comprises using confidence levels to determine whether an object is vegetation, water, soil, or some other object. ### Claim 5 (depends on 1) The method for hyperspectral target identification according to claim 1, further comprising determining whether an object in the image is friend or foe. ### Claim 6 (depends on 1) The method for hyperspectral target identification according to claim 1, wherein the at least four pixels is a 2×2 pixel grouping a 1st pixel is full band of the sensor, a 2nd pixel is 1.05 μm, a 3rd pixel is 1.50 μm, and a 4th pixel at 1.95 μm, wherein the wavelengths are defined by the spectral response of the target and the clutter to be removed. ### Claim 7 (depends on 1) The method for hyperspectral target identification according to claim 1, wherein the method continues by shifting a single pixel and repeating the process, pixel by pixel, row by row across the plurality of pixels on a sensor array. ### Claim 8 (depends on 1) The method for hyperspectral target identification according to claim 1, further comprising using automatic target recognition (ATR) to differentiate between target types based on a priority list. ### Claim 9 (depends on 1) The method for hyperspectral target identification according to claim 1, further comprising using a fusion approach comprising additional sensor data and post processing. ### Claim 10 A method for hyperspectral target identification, comprising: mapping specific wavelengths and bandwidths to each of a plurality of pixels on an FPGA sensor array; calculating ratios between pixel pairs from the plurality of pixels on an FPGA sensor array, wherein at least four pixels (2×2) are used for calculating ratios and one of the four pixels is used as a reference pixel; and eliminating clutter from an image comprising the plurality of pixels on an FPGA sensor array based on the calculated ratios using contrast spatial filters to provide real-time processing. ### Claim 11 (depends on 10) The method for hyperspectral target identification according to claim 10, wherein the elimination of clutter include the removal of vegetation from the image. ### Claim 12 (depends on 10) The method for hyperspectral target identification according to claim 10, wherein the elimination of clutter from the image further comprises using confidence levels to determine whether an object is vegetation, water, soil, or some other object. ### Claim 13 (depends on 10) The method for hyperspectral target identification according to claim 10, further comprising determining whether an object in the image is friend or foe. ### Claim 14 (depends on 10) The method for hyperspectral target identification according to claim 10, wherein in a 2×2 pixel grouping a 1st pixel is full band of the sensor, a 2nd pixel is 1.05 μm, a 3rd pixel is 1.50 μm, and a 4th pixel at 1.95 μm, wherein the wavelengths are defined by the spectral response of the target and the clutter to be removed. ### Claim 15 (depends on 10) The method for hyperspectral target identification according to claim 10, wherein the method continues by shifting a single pixel and repeating the process, pixel by pixel, row by row across the plurality of pixels on an FPGA sensor array. ### Claim 16 (depends on 10) The method for hyperspectral target identification according to claim 10, further comprising using automatic target recognition (ATR) to differentiate between target types based on a priority list. ### Claim 17 (depends on 10) The method for hyperspectral target identification according to claim 10, further comprising using a fusion approach comprising additional sensor data and post processing. ### Claim 18 A computer program product comprising one or more non-transitory machine-readable mediums encoded with instructions that when executed by one or more processors cause a process to be carried out for hyperspectral target identification, the process comprising: mapping specific wavelengths and bandwidths to each of a plurality of pixels on an FPGA sensor array; calculating ratios between pixel pairs from the plurality of pixels on an FPGA sensor array, wherein at least four pixels (2×2) are used for calculating ratios and one of the four pixels is used as a reference pixel; and eliminating clutter from an image comprising the plurality of pixels on an FPGA sensor array based on the calculated ratios using contrast spatial filters to provide real-time processing.