A fractal-based image compression algorithm under wavelet transformation for hyper-spectral remote sensing image was introduced in this paper (also named AWFC algorithm). With the development of the hyperspectral remote sensing we have to obtain more and more spectral bands and how to store and transmit the huge data measured by TB bits level becomes a disaster to the limited electrical bandwidth. It is important to compress the huge hyperspectral image data acquired by hyperspectral sensor such as MODIS, PHI, OMIS etc. Otherwise, conventional lossless compression algorithm couldn't reach satisfied compression ratio while other loss compression methods could get results of high compression ratio but no good image fidelity especially to the hyperspectral image data. As the third generation image compression algorithm-fractal image compression is superior than traditional compression methods with high compression ratio, good image fidelity and less time complexity. In order to keep the spectral dimension invariability, we have compared the results of two compression algorithms based on the outside storage file structure of BSQ and BIP separately. The HV and Quad-tree partitioning and the domain-range matching algorithms have also been improved to accelerate the encode/decode efficiency. The proposed method has been realized and obtained perfect experimental results. At last, the possible modifications algorithm and the limitations of the method are also analyzed and discussed in this paper.
To meet the demand of monitoring water pollution in China, Information Center of State Environmental Protection of China (ICSEP) and Institute of Remote Sensing Applications, Chinese Academy of Sciences (IRSA,CAS) have carried out a project to utilize the data extracted from Environment and Hazard Monitoring Constellation. This project is to build the first Remote-sensing and Environmental Monitoring System (REMS) in China. The most important component of REMS is the Hyperspectral-Environmental Database (HED). This paper is to describe the architecture and mechanism of HED. HED consists of five parts: Environmental backgrounds, Spectrums, Hyperspectral images, Basic geographic information and Environmental quality data. The interactions and relationships among the five parts are depicted. The workflow of HED assisting REMS is delineated. A preliminary research in Taihu Lake based on HED is also described in this paper.
A new architecture for HIPAS (Hyperspectral Image Processing and Analysis System V2.0) was introduced in this paper which was modified and improved based on the first version of HIPAS V1.0. The comprehensive hyperspectral image analyzing system has been developed under VC++6.0 integrated development environment (IDE) and obtained perfect runtime efficiency and stability. The base architecture was specially designed and implemented to meet the requirements for the rapid preprocessing of imaging spectrometer data and easy prototyping of algorithms. Based on the modularized and object oriented software engineering construction, the architecture is compatible for other UNIX platforms with little modification. The most important components of HIPAS were presented in this paper including tools for input/output, preprocessing, data visualization, information extraction, conventional image analysis, advanced tools, and integrated interface to connect with general spectral databases. Some new methodologies for data analysis and processing were realized and applied to reach some valuable results based on the architecture including mineral identification, agriculture investigation, urban mapping etc. With an open storage architecture, HIPAS is entirely compatible with some advanced special commercial software such as ENVI and ERDAS and even the common image processing system Photoshop. At last, a strict and careful software test was carried out and the results were also analyzed and discussed.