Hyperspectral imaging technology provides solutions for camouflage identification - Database & Sql Blog Articles

Sichuan Shuangli Hepu Technology Co., Ltd., a subsidiary of Zhuoli Hanguang, tested the camouflage target at the experimental base of the Mulanweichang grassland on July 13-14, 2015, and obtained hyperspectral data of the camouflage target using a hyperspectral imager. Target identification.

Double benefit spectrum equipment:

Hyperspectral imager;

Spectral range: two cameras in the 400nm-1000nm and 1000nm-2500nm bands, spectral resolution: (@400-1000nm), 11.9nm@1129nm (@1000-2500nm); target distance: 50m-2000m; test time: 14th 10:30-14:30 am;

1. Test with a short-wave infrared camera:

Figure 1 Mounting helicopter

Figure 2 Instrument equipment

The target is captured by a short-wave infrared camera, and the corresponding camouflage target image is obtained. As shown in the following figure, the spectral range is from 1000 nm to 2500 nm. The red circle is marked as a camouflage net. Using a short-wave infrared camera, the image of the camouflage target can be directly captured and distinguished from other background objects.

Green targets such as green vegetation and trees and camouflage targets are displayed in green under the overall environment, while shooting with short-wave infrared cameras can distinguish real green vegetation (or trees, grasses) from camouflage targets.

Figure 3 Short-wave infrared hyperspectral camera capture image (RGB image)

In Fig. 4 and Fig. 5, the image after data processing on the original data can also distinguish the image of the target object from the background, and the location of the camouflage network as shown in the figure.

Figure 4 PCA algorithm processed results

Figure 5 PCA algorithm after processing results

Select 9 objects in the image, which are camouflage nets 1-6, trees, trees, and houses as objects, and obtain their corresponding spectral curves, as shown in the figure.

Figure 6 Spectral curves of different targets

The spectral curve corresponding to the camouflage net 12346 is basically the same. The spectral curve of the camouflage net 5 is very different from the other five. This camouflage net is a camouflage net specially provided by the National Defense Science and Technology University, which is similar to the spectral curve of the tree, but more than the vegetation. The reflectance is high, and the same spectral absorption peak exists at 2061 nm as the other five camouflage nets.

Figure 7 Spectral curves for different targets

Figure 8 Camouflage & Trees & Car Target Recognition

Figure 9 Spectrum curve of the target

Trees, cars, and camouflage nets all have their own characteristic peak positions, and specific algorithms can be used to classify and identify these objects.

2. Test with a visible-near infrared camera:

Figure 1 Hyperspectral imager

Image acquisition of camouflage nets using a visible-near-infrared hyperspectral camera with a spectral range of 400 nm to 1000 nm and a spectral resolution of 4 nm.

Figure 2 visible - near-infrared hyperspectral camera shooting

Figure 3 Identification results after algorithm processing

Figure 4 Spectrum curve

Selecting different target objects and obtaining the corresponding spectra, the reflectance of the vegetation spectrum will increase after 680 nm, and the red edge effect exhibited by non-vegetation is very different from the red edge effect of real vegetation.

The data in the visible-near-infrared band is processed, and the normalized vegetation index and the red-edge normalized vegetation index are used to distinguish and distinguish the target.

1. The Normalized Difference Vegetation Index (NDVI) calculation can transform multispectral data into a single image band for displaying vegetation distribution. The difference between the non-vegetation target and the vegetation can be found. After the camouflage net hidden in the vegetation is processed by the normalized vegetation index processing algorithm, it is obvious that the real vegetation can be distinguished.

Figure 5 Single-band image

Standard algorithm:
NDVI=(ρ_Nir-ρ_Red)/(ρ_Nir+ρ_Red )
The center wavelength of the specified band: ρ_Nir=800nm; ρ_Red=680nm

Figure 6 RGB diagram

All the targets in the image are processed by corresponding algorithms, and the exponential coefficients are arranged in order from low to high, and detailed division is carried out.

Figure 7 classification recognition results

The normalized vegetation indices of different targets (or vegetation) are different, and the corresponding index coefficients can be obtained by using the vegetation standard algorithm.
The range of NDVI values ​​is between -1 and +1, and the range of general green vegetation is 0.2 to 0.8.

Figure 8 Vegetation classification recognition results

2. Red edge normalization index:

The center wavelength of the specified band: ρ_Nir=750nm; ρ_Red=705nm

Figure 9 classification recognition results

The range of NDVI _705 is between -1 and +1, and the range of general green vegetation is 0.2~0.9.

Figure 10 Vegetation classification recognition results

The red edge effect of vegetation can be used to distinguish between real vegetation and camouflage targets. The camouflage target did not show a very pronounced red edge effect.