ABSTRACT:The Chinese satellites HJ-1 A/B, CBERS-02C and ZY-3 have been recently launched and are considered as the main space platforms on orbit to acquire optical images for monitoring the Earth for various applications in China. The commercially distributed products (Level 1 or 2) of those satellites usually lack sufficient information (about platform, sensor and ephemeris) that is the key to geometrically correct the acquired images. It is therefore always a challenging issue and the first step to assess the geometric accuracy, which is a key part of qualities in spatial data, of the images from those satellites before generation of geometrically accurate image products. This paper first describes an operational methodology to assess the geometric accuracy of those satellite images. The methodology automatically collects dense and spatially well distributed ground control points (GCP) against reference imagery and then fits those GCPs to the given geometric math model. The geometric accuracy of an image can then be assessed from the overall fitness of those GCPs and their distribution of geometric errors along and across track. The residual mean square (RMS) parameter is used to indicate the degree of overall fitness of the GCPs to the photogrammetric system. The distribution of geometric errors may be random or approximated by a second or higher order polynomial functions; the latter case is generally considered as a systematic error that was not removed completely in the Level 1 or 2 data product. In order to draw solid conclusions, a significant number of samples are selected for each of those satellites by taking variations of landscapes into consideration. The assessment experiments demonstrate that the accuracy of HJ-1 A/B is often very poor, that of CBERS-02C is better than the situation of HJ-1 A/B but records poor accuracy for most samples, and that of ZY-3 is the best among all satellites under investigation and has few samples with poor accuracy. According to the assessment results, this paper suggests an operational correction methodology to improve the accuracy for those satellites, particularly for the HJ-1 A/B and CBERS-02C. Operational production proves that the proposed correction methodology is capable of achieving much higher accuracy than traditional ones and the achieved accuracy meets high standard product requirements for such applications as mapping.

The number of Earth observation satellites from China increases dramatically recently and those satellites are acquiring a large volume of imagery daily. As the main portal of image processing and distribution from those Chinese satellites, the China Centre for Resources Satellite Data and Application (CRESDA) has been working with PCI Geomatics during the last three years to solve two issues in this regard: processing the large volume of data (about 1,500 scenes or 1 TB per day) in a timely manner and generating geometrically accurate orthorectified products. After three-year research and development, a high performance system has been built and successfully delivered. The high performance system has a service oriented architecture and can be deployed to a cluster of computers that may be configured with high end computing power. The high performance is gained through, first, making image processing algorithms into parallel computing by using high performance graphic processing unit (GPU) cards and multiple cores from multiple CPUs, and, second, distributing processing tasks to a cluster of computing nodes. While achieving up to thirty (and even more) times faster in performance compared with the traditional practice, a particular methodology was developed to improve the geometric accuracy of images acquired from Chinese satellites (including HJ-1 A/B, ZY-1-02C, ZY-3, GF-1, etc.). The methodology consists of fully automatic collection of dense ground control points (GCP) from various resources and then application of those points to improve the photogrammetric model of the images. The delivered system is up running at CRESDA for pre-operational production and has been and is generating good return on investment by eliminating a great amount of manual labor and increasing more than ten times of data throughput daily with fewer operators. Future work, such as development of more performance-optimized algorithms, robust image matching methods and application workflows, is identified to improve the system in the coming years.

ABSTRACT:ZY-3 has been acquiring high quality imagery since its launch in 2012 and its tri-stereo (three-view or three-line-array) imagery has become one of the top choices for extracting DSM (Digital Surface Model) products in China over the past few years. The ZY-3 tristereo sensors offer users the ability to capture imagery over large regions including an entire territory of a country, such as China, resulting in a large volume of ZY-3 tri-stereo scenes which require timely (e.g., near real time) processing, something that is not currently possible using traditional photogrammetry workstations. This paper presents a high performance computing solution which can efficiently and automatically extract DSM products from ZY-3 tri-stereo imagery. The high performance computing solution leverages certain parallel computing technologies to accelerate computation within an individual scene and then deploys a distributed computing technology to increase the overall data throughput in a robust and efficient manner. By taking advantage of the inherent efficiencies within the high performance computing environment, the DSM extraction process can exploit all combinations offered from a set of tri-stereo images (forward-backword, forward-nadir and backword-nadir). The DSM results merged from all of the potential combinations can minimize blunders (e.g., incorrect matches) and also offer the ability to remove potential occlusions which may exist in a single stereo pair, resulting in improved accuracy and quality versus those that are not merged. Accelerated performance is inherent within each of the individual steps of the DSM extraction workflow, including the collection of ground control points and tie points, image bundle adjustment, the creation of epipolar images, and computing elevations. Preliminary experiments over a large area in China have proven that the high performance computing system can generate high quality and accurate DSM products in a rapid manner.