Target range estimation is traditionally based on radar and active sonar systems in modern combat systems. However, jamming signals tremendously degrade the performance of such active sensor devices. We introduce a simple target range estimation method and the fundamental limits of the proposed method based on the atmosphere propagation model. Since passive infrared (IR) sensors measure IR signals radiating from objects in different wavelengths, this method has robustness against electromagnetic jamming. The measured target radiance of each wavelength at the IR sensor depends on the emissive properties of target material and various attenuation factors (i.e., the distance between sensor and target and atmosphere environment parameters). MODTRAN is a tool that models atmospheric propagation of electromagnetic radiation. Based on the results from MODTRAN and atmosphere propagation-based modeling, the target range can be estimated. To analyze the proposed method’s performance statistically, we use maximum likelihood estimation (MLE) and evaluate the Cramer-Rao lower bound (CRLB) via the probability density function of measured radiance. We also compare CRLB and the variance of MLE using Monte-Carlo simulation.
Image denoising is a fundamental image processing step for improving the overall quality of images. It is more
important for remote sensing images because they require significantly higher visual quality than others. Conventional
denoising methods, however, tend to over-suppress high-frequency details. To overcome this problem, we present a
novel compressive sensing (CS)-based noise removing algorithm using adaptive multiple samplings and reconstruction
error control. We first decompose an input noisy image into flat and edge regions, and then generate 8x8 block-based
measurement matrices with Gaussian probability distributions. The measurement matrix is applied to the first three
levels of wavelet transform coefficients of the input image for compressive sampling. The orthogonal matching pursuit
(OMP) is applied to reconstruct each block. In the reconstruction process, we use different error threshold values
according to both the decomposed region and the level of the wavelet transform based on the fast that the first level
wavelet coefficients in the edge region have the lowest error threshold, whereas the third level wavelet coefficients in
the flat region have the highest error threshold. By applying adaptive threshold value, we can reconstruct the image
without noise. Experimental results demonstrate that the proposed method removes noise better than existing state-ofthe-
art methods in the sense of both objective (PSNR/MSSIM) and subjective measures. We also implement the
proposed denoising algorithm for remote sensing images with by minimizing the computational load.
Korean Multi-purpose Satellite-3A (KOMPSAT-3A), which weighing about 1,000 kg is scheduled to be launched
in 2013 and will be located at a sun-synchronous orbit (SSO) of 530 km in altitude. This is Korea's rst satellite
to orbit with a mid-wave infrared (MWIR) image sensor, which is currently being developed at Korea Aerospace
Research Institute (KARI). The missions envisioned include forest re surveillance, measurement of the ocean
surface temperature, national defense and crop harvest estimate.
In this paper, we shall explain the MWIR scene generation software and atmospheric compensation techniques
for the infrared (IR) camera that we are currently developing. The MWIR scene generation software we have
developed taking into account sky thermal emission, path emission, target emission, sky solar scattering and
ection based on MODTRAN data. Here, this software will be used for generating the radiation image
in the satellite camera which requires an atmospheric compensation algorithm and the validation of the accuracy
of the temperature which is obtained in our result.
Image visibility restoration algorithm is a method for removing the eect of atmosphere between the camera
and an object. This algorithm works between the satellite and the Earth, to predict object temperature noised
with the Earth's atmosphere and solar radiation. Commonly, to compensate for the atmospheric eect, some
softwares like MODTRAN is used for modeling the atmosphere. Our algorithm doesn't require an additional
software to obtain the surface temperature. However, it needs to adjust visibility restoration parameters and the
precision of the result still should be studied.