Junho Yeom

Hybrid Pansharpening Algorithm for High Spatial Resolution Satellite Imagery to Improve Spatial Quality

Most pansharpened images from existing algorithms are apt to present a tradeoff relationship between the spectral preservation and the spatial enhancement. In this letter, we developed a hybrid pansharpening algorithm based on primary and secondary high-frequency information injection to efficiently improve the spatial quality of the pansharpened image. The injected high-frequency information in our algorithm is composed of two types of data, i.e., the difference between panchromatic and intensity images, and the Laplacian filtered image of high-frequency information. The extracted high frequencies are injected by the multispectral image using the local adaptive fusion parameter and postprocessing of the fusion parameter. In the experiments using various satellite images, our results show better spatial quality than those of other fusion algorithms while maintaining as much spectral information as possible.

Detecting damaged building parts in earthquake-damaged areas using differential seeded region growing

ABSTRACT Acquiring information about earthquake-damaged buildings is essential for effective rescue and restoration operations. Building damage must be assessed to provide detailed information regarding the location and proportion of damage to individual buildings. Automatic processing of damage assessment is also critical in hastening relief efforts. Therefore, we propose a new method for automatically extracting damaged building parts and quantitatively assessing the damage to individual buildings caused by earthquakes. The proposed method consists of four parts: generating differential information, differential seeded region growing (DSRG), rule-based earthquake damage analysis, and accuracy assessment. First, differential information is automatically derived to extract the damage candidates. The damage candidates are then used as seed points for the region growing process to extract damaged building parts without requiring intervention by a human analyst. Then, designed automated extraction rules based on the condition of the collapsed or crushed buildings are used on the DSRG results. We applied the proposed method to both a residential area and a business area in Port-au-Prince, Haiti, and evaluated its accuracy using a visual comparison, a location-based assessment, and a proportion-based assessment. The results of the visual comparison were similar to the reference data, exhibiting location accuracies of 86% and 89% for the chosen residential and business areas, respectively. An assessment of the damage proportion to individual buildings was performed, which showed that the proposed method achieved accuracies of 81% and 84% for the residential and business areas, respectively, and was highly correlated with the reference data. The proposed method can accurately estimate damaged building parts, which can accelerate rapid relief actions in earthquake-damaged areas. In addition, the proposed method promotes cost-effective relief actions because it filters out many intact buildings without omitting damaged buildings.

Automatic registration of high-resolution optical and SAR images based on an integrated intensity- and feature-based approach

Precise image-to-image registration is required to use multi-sensor data implementing a diversity of applications related with remote sensing. The purpose of this paper is to develop an automatic algorithm that co-registers high-resolution optical and SAR images based on an integrated intensity-and feature-based approach. As a pre-registration step, initial differences between the translation of the x and y directions between images were estimated with the Simulated Annealing optimization method using Mutual Information as an objective function. After the pre-registration, the line features were extracted to design a cost function that finds matching features based on the similarities of their locations and gradient orientations. Only one feature at each regular grid region having a minimum value of cost function was selected as a final matching point to extract the large number of well-distributed points. The final points were then used to construct a transformation combining the piecewise linear function with the affine transformation to increase the accuracy of the geometric correction.

Line-based paddy boundary extraction using the RapidEye satellite image

The crop boundary data provide the basic information for agricultural management. In this study, line-based boundary enhancement and extraction methods are proposed to delineate detailed paddy boundaries. Line extraction is performed based on the boundary enhancement result and Hough line extraction using RapidEye satellite image. The proposed line-based method is efficient in detecting the paddy boundaries while preserving linearity. In addition, the proposed method adopts an automated boundary detection process except for line editing. Therefore, the proposed method economically provides information on the paddy boundaries that can be utilized as the basic spatial unit for yield analysis, ownership management, and precision farming.

Usefulness assessment of polarimetric parameters for line extraction from agricultural areas

In many agricultural applications, PolSAR data are widely used because they can be decomposed into various scattering components, which can be of help in observing the characteristics of agricultural areas. Recently, studies have been conducted to find suitable polarimetric parameters for specific applications. This paper tried to find appropriate polarimetric parameters for line extraction from agricultural areas as the line features are among the basic features of the surface. Towards this end, various polarimetric parameters were produced using polarimetric decomposition methods. LSD was used to extract lines from each polarimetric parameter image over agricultural areas, without any threshold value selection. The comparison of the line extraction result from each polarimetric parameter with the others was conducted through quantitative evaluation. Through this process, three parameters of the Pauli decomposition is found to be the suitable polarimetric parameters for line extraction from agricultural areas.

A regular grid-based Hough transform for the extraction of urban features using high-resolution satellite images

The Hough transform (HT) is a widely used line extraction method to detect the boundary of urban features. However, the HT has some problems, such as high computational costs and omissions of lines in remote sensing images. In this study, a robust and improved Hough line extraction method, which uses a regular grid and adjacent information from the base grid cell and its neighbouring grid cells, is proposed. The proposed method efficiently delineates the lines of urban features, without any line omissions. The regular grid aids the direct determination of the location and size of the transform region and decreases the computational cost. In addition, the adjacent information is useful for line connections and removes the need for complicated geometric factors. The proposed regular grid-based Hough transform (RGHT) was compared with the standard Hough transform (SHT). The results showed that the proposed method extracted evenly distributed lines over the entire image at a low computational cost. Furthermore, the proposed method extracted rectangular and curved lines of buildings and roads sites better than the SHT method, without omitting portions of the urban features.

Object-based classification and building extraction by integrating airborne LiDAR data and aerial image

It is generally difficult to classify an object type having different colors into the same class using only optical data such as a satellite or aerial image. This paper proposes a method that solves this problem by combining LiDAR data and an aerial image. The method extracts building pixels from LiDAR data and then identifies building objects on the aerial image by overlaying the LiDAR result to a segmented aerial image through the definite rule. This process plays a role in transforming building objects of LiDAR data to ones of the aerial image.