Chih-Yuan Chu

Band selection for hyperspectral images based on impurity function

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

A high precision FPGA-based active radar calibrator

This study design a high precision active radar calibrator (ARC) with programmable echo points which served as control points for both geometric and radiometric calibrations or other applications. The echo delay and Doppler shift of ARC system are fully digitally controlled. Specification and performance of the digital ARC were tested on C-band ERS-2 SAR image. From the experimental results, the FPGA circuit can produce several calibration points of desired range or azimuth direction. In InSAR application, the echo points of ARC system can estimate squint angle difference in two co-registration images. Compared to analog time delay ARC system, the current ARC generates delay error less than 10ns, greatly reduced from 30–50 ns that analog time delay has. In addition, the stability of the time delay and output power in the FPGA based ARC are both much better than the traditional analog delay ARC.

Color Enhancement for Four-Component Decomposed Polarimetric SAR Image Based on a CIE-Lab Encoding

Color enhancement of decomposed fully polarimetric synthetic aperture radar (PolSAR) image is vital for visual understanding and interpretation of the polarimetric information about the target. It is common practice to use RGB or HIS color space to display the chromatic information for polarization-encoded, Pauli-basis images, or model-based target decomposition of PolSAR images. However, to represent the chroma for multi-polarization SAR data, the region of basic RGB color space does not fully cover the human perceptual system, leading to information loss. In this paper, we propose a color-encoding framework based on the CIE-Lab, a perceptually uniform color space, aiming at a better visual perception and information exploration. The effective interpretability in increasing chromatic, and thus visual enhancement, is presented using extensive datasets. In particular, the four decomposed components—volume scattering, surface scattering, double bounce, and helix scattering—along with total return power, are simultaneously mapped into the color space to improve the discernibility among the scattering components. The five channels derived from the four-component decomposition method can be simultaneously mapped to CIE-Lab color space intuitively. Results show that the proposed color enhancement not only preserves the color tone of the polarization signatures, but also magnifies the target information embedded in the total returned power.

A modified adaptable nearest feature space classifier for remote sensing images

In this paper, a novel technique, known as a modified adaptable nearest feature space (MANFS) classifier, is proposed for supervised classification of remote sensing images. The original nearest feature space (NFS) may cause misclassification if the test samples are close to the different class training samples which are highly overlapped. Thus it is difficult to discriminate different classes. Compared to the original NFS classifier, we propose a novel MANFS classifier, which can precisely analysis the coverage of the feature space used in NFS and perfectly confine the extensible ranges of each feature space of NFS, to reduce the impact of the overlapping training samples of different categories. Experimental results show that MANFS achieves better classification accuracy than the original NFS one for remote sensing image.

A new client-server architecture for real-time Micro-Movement ground based SAR system

Summary form only given. Micro Movement iMager (MMI), a new solution of ground based SAR system, for multi-purpose monitoring specific region of interest. All kind of disaster including the natural hazard or human-instigated disaster can be monitored by the embedded programming functions such as the re-projection, landslide change detection, elevation change detection, real-time warning alarm and the temporal analysis for entire database. The integration of hardware and software was introduced in this paper for the purpose of operation, database access and maintenance. According to the server-client architecture, the system was split into three parts. First is the data acquisition hardware that combines a controlling system. Some characteristics such as scheduling, self-calibration and remote controlling are embraced in this system. Another is the bridge system including the database, command centre and database analysis capabilities. Because that all command and analysis were passed through this bridge system by the client, the computing capabilities, data access effectiveness and convenience of database management had be considerate. All kind of real-time and stored data can be accessed and analyzed on the fly without any redundant data transfer. The last part is the user-friendly client interface and deformation application which shown in Figure 1. All functions are built with object oriented and easy-to-use purpose reducing unnecessary operations including the hardware controlling, analysis and visualization. According to the interactive visualization toolkit, the well-analyzed information was presented with two or three dimension widget fusing DEM and aerial photo. All of operation were implemented and evaluated in chamber and real testing in-situ environment. This architecture was also verified that is suitable for the indoor and outdoor environment. The outdoor real data, which obtained at Cameron highland in Malaysia and Meihua Village in Taiwan, were demonstrated in this paper.

High-performance adaptive local kriging applied to recovering surface deformation associated with the fault zones

Differential Interferometric Synthetic Aperture Radar (DInSAR) is an effective technique to measures the surface displacement caused by strong earthquakes. In our previous work an adaptive local kriging (ALK) was proposed to recover surface deformation associated with the fault zones. The calculation of ALK needs a huge computing power. Thus a high-performance computing is needed. It can not only speedup the interpolation processes of ALK but also handle large volumes of widely distributed remote sensing dataset. As a result, a parallel image interpolation approach, referred to as the graphics processing unit (GPU) based ALK method, to the slant range motion maps derived by DInSAR is proposed in this paper. It makes use of the performance profiling to analyze the serial version of ALK and perform a parallel GPU computation for reducing the computation time efficiently. By employing the NVIDIA TITAN GPU, the proposed method achieves a speedup of 77.86× compared to its CPU counterpart part.

Rain effect variability analysis in Taiwan

A two year observation (2002-2003) using two 2D optical distrometers at different locations were recorded. The DSD were measured and analyzed for different seasons under various rain rates. It follows that a relationship between rain rate and DSD was established. From statistical regression, it was also found that the DSD follows the Gamma distribution best in most cases. Long-term rain attenuation measurements using a Ka band (28GHz) CW system at vertical polarization were conducted in northern Taiwan. An optical rain gauge which has resolution of 0.01mm and can collect the rain rate every 5 seconds measured the rain rate at the same location. The attenuation due to rain can be estimated by calculating the extinction coefficient over all of the rain drops within the antenna beam volume. Two methods were used to estimate the extinction coefficient. First, assuming that the scattering mechanism follows the Mie scattering approximation, and the rain drops are all sphere. Second, using T-Matrix method and the oblateness of the rain drops vary from 1.0 to 0.8. Making use of the Gamma-distribution for DSD model, a semi-empirical rain attenuation model was then developed. To validate the model, we compared it with the measured data. For the purpose of cross referencing, we also compared it with the ITU-R and Crane models. The results show that the proposed model matched very well with in-situ measurement from a two-year data set. Both the Crane model and the ITU-R model were inadequate, as expected, for a correct interpretation of the accumulated measurement data producing overestimates.