Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features

Abstract

With the resolution improvement of the remote-sensing images, more details are shown clearly. The challenge that comes along is how to boost the relatively low classification accuracy caused by using pixel-based image classification approaches and low-level visual structure. The low-level features (LLF) may not well describe the image due to the semantic gap between low-level visual features and high-level semantics of images. The bag-of-visual-words (BOV) model which generates mid-level features was proposed to bridge the two levels. However, it generally neglects the context information between local patches. In this paper, an object-oriented semantic classification algorithm that combines BOV with the optimal segmentation scale is presented. In this algorithm, BOV addresses the problem of the representation of mid-level for scenes, while the optimal segmentation scale intends to overcome the defect of conventional BOV in lacking of relationship between image patches and to give more thorough description. The object-based BOV is presented to construct mid-level representations for object description instead of LLF, and histogram intersection kernel (HIK) is introduced in support vector machine (SVM) for classification. The experiments conducted on three datasets testify the superiority of the proposed algorithm.