Development of a NOAA image database with feature-based retrieval functions
Changming Zhou and Mikio Takagi
Institute of Industrial Science
University of Tokyo, Tokyo, Japan
Abstract
In this paper, NOAA-AVHRR image database system which is being developed in our laboratory is presented. Some new image retrieval approaches which are based on the image features are included in this system. After one scene of NOAA image is received, an automatic classification method proposed in this paper is applied to a 2048 pixels x 1960 liner region, which usually arouses users' interest, around Japan. The classified image, them is processed with region-labeling and boundary-following method. Each labeled region is represented using chain codes and stored in the system. The spatial relations between regions in the same scene are described by a syntactic pattern recognition approach, i.e., an edge-labeled directed node-label controlled graph (ed NLC-graph) based on the center-of-mass of each region in addition to a men-driven user interface, the system provides its users with two kinds of guide images, and users may use a mouse tool to specify the objective region and conditions of image contents (e.g., with or without clouds, the shapes of clouds etc.) on a display instrument, and retrieve images in the following to steps; (1) global retrieval based on the spatial relation between regions represented by ed NLC-graph, and (2) similarity retrieval based on the geometric properties of the dominant regions represented by chain codes. In addition, in order to improve the retrieval speed, and since information about cloud-covered regions is the main retrieval clue usually given by users, bit-based operations functions of a general-purpose image. Processor are applied to an index image generated from original images to do pre-selection of the candidate images before the above two-step retrieval processing.
Introduction
In most of the remotely sensed image database systems developed up to now, images are recalled mainly by the attribute information attached to the archived images such as image identities, sensor name etc. Nowadays, however because remotely sensed images are integrated into geographic information systems (GIS) together with various maps and other data, more effective retrieval approaches, for instance retrieval methods based on the image contents, are required. Development of such kind of retrieval methods becomes the key subject of image database and GIS.
In the case of NOAA images, considerable receiving, archiving and procession systems have been developed in many ground stations (1) (3) There exit a few systems that distribute a kind of abstract images called quick-look and provide users with visual inspection after images are received [1][4]. In [1] raw images are classified into a few classes such as land, sea etc. denoted as hatched patterns with a simple classification method based on some thresholds obtained empirically, and in [4], 10-bit original images are reduced into 8-bit images with size of 512 x 480 pixels, moreover, the reduced images are transformed into dither images with 64 levels using a general-purpose image processor and delivered to other universities and research organizations immediately after images are received via facsimile. In all he systems mentioned above, images can be recalled by using actuations time and some geographic parameters (longitude, latitude etc.), however retrieval approaches based on image features are not offered.
NOAA-AVHRR (Advanced Very High Resolution Radiometer) images are widely using in many fields. About 4-- 8 scenes can be received one day from two NOAA satellites (at present, NOAA-10 and NOAA-11 are available). Resembling to other remotely sensed data, NOAA-AVHRR images possess the characteristics of wide coverage, frequent observation, vast quantities, and are utilized for monitoring and observing the environment of the Earth. Consequently, users usually access those images that are received under some special conditions and possess some features. This kind of access can not be realized only by those attribute data of images managed by conventional database management system, e.g. the relational database management system.
The system presented in this paper is developed mainly to provide users with not only conventional retrieval approaches but also those based on image features. Because processing such as geometric distortion correction and sensor calibration of NOAA-AVHRR image is very time-consuming and space-occupying, only raw NOAA data are traded in this system. Structural and syntactic pattern recognition methods, iconic indexing and similarity retrieval approaches are introduced into this system for feature extraction, description, representation and image feature retrieval of NOAA-AVHRR images.
