The rich data provided by high-resolution satellite imagery allow us to directly model aerial
scenes by understanding their spatial and structural patterns. While pixel-and object-based …

This paper investigates local invariant features for geographic (overhead) image retrieval.
Local features are particularly well suited for the newer generations of aerial and satellite …

Spatial data mining is the process of discovering interesting and previously unknown, but
potentially useful patterns from the spatial and spatiotemporal data. However, explosive …

Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by
ever-expanding cities and become so-called urban villages (UVs) with substandard living …

The new generation of Earth observation sensors with high spatial resolution can provide
detailed information for change detection. The widely used methods for high-resolution …

With the emergence of huge volumes of high-resolution remote sensing images produced
by all sorts of satellites and airborne sensors, processing and analysis of these images …

This paper proposes a novel unsupervised feature learning algorithm for land-use scene
recognition on very high resolution remote sensing imagery. The proposed technique …

In this paper, the task of unsupervised visual object categorization (UVOC) is addressed. We
utilize a variant of Self-organizing Map (SOM) to cluster images in two different scenarios …

It still remains a big challenge to accurately identify the geospatial objects with well-
regulated outlines within remote sensing (RS) images such as residential buildings, factory …

Remote sensing imagery has been commonly used by intelligence analysts to discover
geospatial features, including complex ones. The overwhelming volume of routine image …