Abstract
Remote-sensing images play a crucial role in a wide range of applications and have been receiving significant attention. In recent years, great efforts have been made in developing various methods for intelligent interpretation of remote-sensing images. Generally speaking, machine learning-based methods of remote-sensing image interpretation require a large number of labeled samples and there are still not enough annotated datasets in the field of remote sensing. However, manual annotation of remote-sensing images is usually labor-intensive and requires expert knowledge and the accuracy of annotation results is relatively low. The goal of this paper is to propose a novel tile-level annotation method of remote-sensing images to obtain remote-sensing datasets which are well-labeled and contain accurate semantic concepts. Firstly, we use a set of images with defined semantic concepts to represent the training set and divide them into several nonoverlapping regions. Secondly, the color features, texture features, and spatial features of each region are extracted, and discriminative features are obtained by the weight optimization feature fusion method. Then, the features are quantized into visual words by applying a density-based clustering center selection method and an isolated feature point elimination method. And the remote-sensing images can be represented by a series of visual words. Finally, the LDA model is used to calculate the probabilities of semantic categories for each region. The experiments are conducted on remote-sensing images which demonstrate that our proposed method can achieve good performance on remote-sensing image tile-level annotation. The implications of our research can obtain annotated datasets with accurate semantic concepts for intelligent interpretation of remote-sensing images.
Highlights
As a widely used emerging technology, remote-sensing technology is closely connected with spatial geography, photoelectric information, and other disciplines and has become a relatively important part of modern science, as well as an important technical means to study the earth resources and environment [1]
We counted the categories of all annotation results in the corresponding positions, and the category with the highest number of occurrences was adopted as the category of the region. e number of topics for water, farmland, vegetation, and residential area are 50, 20, 50, and 40, respectively. e second dataset is a large-scale classification set of Gaofen image dataset (GID). is dataset contains 150 pixel-level annotated GF-2 images with the size of 7200 × 6800 and contains 5 categories [46]
The level of the image pyramid for pyramid histograms of oriented gradients (PHOG) is set to 1, and the total number of the visual words is set to 300, which shows good performance in our empirical study. e size of the region is set to 30 × 30 in order to obtain the best efficiency of the algorithm
Summary
As a widely used emerging technology, remote-sensing technology is closely connected with spatial geography, photoelectric information, and other disciplines and has become a relatively important part of modern science, as well as an important technical means to study the earth resources and environment [1]. With the growing number of remote-sensing images, efficient content extraction and scene annotation that can help us quickly understand the huge-size image are becoming increasingly necessary. Machine learning-based methods, which achieve many improvements in many research fields, have been widely applied in remote-sensing image classification and object recognition [2]. Machine learning-based methods require large amounts of manually annotated training data. Since the image data delivered by remotesensing technology usually has a large size, expansive human efforts are usually needed to annotate them manually. Due to cognitive differences, the manual annotation of remote-sensing images usually results in large errors. An effective annotation method is strongly required in remote-sensing applications. It can be applied to image retrieval systems to retrieve image data of specific content from databases
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