Doochun Seo

Contrast Enhancement Using Dominant Brightness Level Analysis and Adaptive Intensity Transformation for Remote Sensing Images

This letter presents a novel contrast enhancement approach based on dominant brightness level analysis and adaptive intensity transformation for remote sensing images. The proposed algorithm computes brightness-adaptive intensity transfer functions using the low-frequency luminance component in the wavelet domain and transforms intensity values according to the transfer function. More specifically, we first perform discrete wavelet transform (DWT) on the input images and then decompose the LL subband into low-, middle-, and high-intensity layers using the log-average luminance. Intensity transfer functions are adaptively estimated by using the knee transfer function and the gamma adjustment function based on the dominant brightness level of each layer. After the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. Although various histogram equalization approaches have been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques. The proposed method can effectively enhance any low-contrast images acquired by a satellite camera and is also suitable for other various imaging devices such as consumer digital cameras, photorealistic 3-D reconstruction systems, and computational cameras.

Wavelength-Adaptive Dehazing Using Histogram Merging-Based Classification for UAV Images

Since incoming light to an unmanned aerial vehicle (UAV) platform can be scattered by haze and dust in the atmosphere, the acquired image loses the original color and brightness of the subject. Enhancement of hazy images is an important task in improving the visibility of various UAV images. This paper presents a spatially-adaptive dehazing algorithm that merges color histograms with consideration of the wavelength-dependent atmospheric turbidity. Based on the wavelength-adaptive hazy image acquisition model, the proposed dehazing algorithm consists of three steps: (i) image segmentation based on geometric classes; (ii) generation of the context-adaptive transmission map; and (iii) intensity transformation for enhancing a hazy UAV image. The major contribution of the research is a novel hazy UAV image degradation model by considering the wavelength of light sources. In addition, the proposed transmission map provides a theoretical basis to differentiate visually important regions from others based on the turbidity and merged classification results.

Push-Broom-Type Very High-Resolution Satellite Sensor Data Correction Using Combined Wavelet-Fourier and Multiscale Non-Local Means Filtering

In very high-resolution (VHR) push-broom-type satellite sensor data, both destriping and denoising methods have become chronic problems and attracted major research advances in the remote sensing fields. Since the estimation of the original image from a noisy input is an ill-posed problem, a simple noise removal algorithm cannot preserve the radiometric integrity of satellite data. To solve these problems, we present a novel method to correct VHR data acquired by a push-broom-type sensor by combining wavelet-Fourier and multiscale non-local means (NLM) filters. After the wavelet-Fourier filter separates the stripe noise from the mixed noise in the wavelet low- and selected high-frequency sub-bands, random noise is removed using the multiscale NLM filter in both low- and high-frequency sub-bands without loss of image detail. The performance of the proposed method is compared to various existing methods on a set of push-broom-type sensor data acquired by Korean Multi-Purpose Satellite 3 (KOMPSAT-3) with severe stripe and random noise, and the results of the proposed method show significantly improved enhancement results over existing state-of-the-art methods in terms of both qualitative and quantitative assessments.

Absolute misalignment estimation with respect to a scanning image sensor

This letter proposes a simple but effective method for in-flight estimation of absolute misalignment, which is defined as the orientation offset error between attitude sensors and an imaging payload. The method was developed for high-resolution spaceborne/airborne imaging systems, which use a scanning image sensor in their payload. This type of image sensor operates in the pushbroom mode, so that previously existing methods are not easily applicable. The proposed method utilizes the pre-existing attitude-determination algorithms originally used to estimate the attitude of a star tracker equipped with a planar image sensor. We converted the geo-referencing relation between the GCP (Ground Control Point) directions and their image vectors into a new expression which enables the application of the attitude-determination algorithms to identify the absolute misalignment. Pointing knowledge analysis, using actual in-flight data, is presented to verify the developed method.

Range estimation of passive infrared targets through the atmosphere

Abstract. 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.

Compressive sensing-based image denoising using adaptive multiple samplings and reconstruction error control

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.

The establishment of a virtual topography model for the development of a helicopter simulator

− The main objective of this study is to establish the virtual topography model for a KA-32 Helicopter simulator, using image data of KOMPSAT-1 EOC and Landsat TM and digital maps drawn to a scale of 1:5,000 by the National Geographic Information Institute. The simulator is a full-size replica of a flight deck assemble of an aircraft of a specific type, model and series inclusive of its equipment, computer program necessary to represent the aircraft on ground and in flight operation, a visual system providing an out-of-the flight deck view, and a force cueing motion system. For this purpose, the following four experiments were conducted; 1) the development of the unified database system and designing of the data base fields of satellite images, DEMs(Digital Elevation Model) and digital maps for a simulator visual system, 2) the generation of DEM about 20km × 20 km, 3) the fusion of KOMPSAT-1 EOC and Landsat TM for textual information of terrain features, and 4) the generation of Ortho-images using GCPs, satellite header information, and bundle adjustment which uses the collinearity condition as the basis for formulating the relationship between the image space and ground space.

Calculation of agricultural land flooding disaster area by Typhoon from KOMPSAT-1 EOC satellite image data

Recently satellite images are increasingly used in the areas of detection, prevention and recovery of natural disasters, because it enables not only periodic observations of wide areas and subsequent comparison and analysis of the situations prior and post-disaster, but also access to otherwise inaccessible terrain. The main objective of this study is to observe and extract relevant geographical information of areas afflicted with floods using KOMPSAT-1 EOC image data