Information mining in remote sensing image archives: system concepts

Abstract

In this paper, we demonstrate the concepts of a prototype of a knowledge-driven content-based information mining system produced to manage and explore large volumes of remote sensing image data. The system consists of a computationally intensive offline part and an online interface. The offline part aims at the extraction of primitive image features, their compression, and data reduction, the generation of a completely unsupervised image content-index, and the ingestion of the catalogue entry in the database management system. Then, the user's interests-semantic interpretations of the image content-are linked with Bayesian networks to the content-index. Since this calculation is only based on a few training samples, the link can be computed online, and the complete image archive can be searched for images that contain the defined cover type. Practical applications exemplified with different remote sensing datasets show the potential of the system. Index Terms-Content-based image retrieval (CBIR), image information mining, information extraction, statistical learning.

An Extension of an Ontology-Based Land Cover Designation Approach for Fuzzy Rules

“…Previous research underlined the importance of using explicit semantics for content based image indexing (CBIR) (DATCU 2003) and (DURBHA 2005). A few research initiatives focused on using the concepts of semantics to enhance image interpretation.…”

Image Mining Using Directional Spatial Constraints

“…w 2 otherwise (11) 1 The landscape is denoted as βα to simplify the notation in this section. Any definition and structuring element from Section II can be used for β. using these spatial priors.…”

“…In addition to a large number of content-based retrieval systems proposed in the computer vision literature, several systems have been specifically designed for mining Earth observation data. For example, Datcu et al [1] developed a system where users can train Bayesian classifiers for a particular concept (e.g., water) using positive and negative examples of pixels, and can have image tiles ranked according to the coverage of this concept estimated using pixel level models. Shyu et al [2] developed an extensive system that supports both tile-based and object-based indexing.…”

Validation of a Hyperspectral Content-Based Information Retrieval (RS-CBIR) System Upon Scarce Data

“…There have been several efforts along this decade to develop CBIR tools for remote sensing images. The main focus has been multispectral and synthetic aperture radar (SAR) images [8,15,16,6,5]. Exploitation of the spectral information provided by hyperspectral sensors by CBIR systems has not been deeply pursued although there are some instances in the literature [10,14,19].…”