UHD Aerial Photograph Categorization by Leveraging Deep Multiattribute Matrix Factorization

Abstract

There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with ultra-high-definitions (UHD) and may contain tens to hundreds of ground objects ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., vehicles and rooftops). Understanding the multiple categories of a rich variety of UHD aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a novel multi-label UHD aerial photo categorization pipeline, wherein the key is to topologically represent the spatial layouts of the ground objects and further deeply encode them using a deep multi-clue matrix factorization (DMCMF) that robustly handles noisy labels at image-level. More specifically, for each UHD aerial photo, we extract visually/semantically salient object patches inside it. To explicitly encode their spatial layout, we construct a graphlet by linking the spatially adjacent object patches into a small graph. Subsequently, a binary MF is designed to intelligently exploit the semantics of these graphlets, wherein four clues: i) binary hash codes learning, ii) noisy labels refinement, iii) deep image-level semantics, and iv) adaptive data graph updating are incorporated. Such DMCMF can be solved iteratively and each graphlet is then converted into the discrete hash codes. Finally, the hash codes corresponding to graphlets within each UHD aerial photo are quantized into a feature vector by a kernel machine for multi-label categorization. Toward a comprehensive comparative study, we complied a million-scale UHD aerial photo set collected from 100 top-ranking cities worldwide. Experiments have shown that 1) our method is highly competitive in learning categorization model from imperfect labels at image-level, and 2) the four clues are elaborately designed and seamlessly combined to learn hash codes for representing UHD aerial photos.

Topics & Concepts