To obtain a complete representation of scene information in high spatial resolution remote sensing scene images, an increasing number of studies have begun to pay attention to the multiple low-level feature types-based bag-of-visual-words (multi-BOVW) model, for which the two-phase classification-based multi-BOVW method is one of the most popular approaches. However, this method ignores the information of feature significance among different feature types in the score-level fusion stage, thus affecting the classification performance of the multi-BOVW methods. To address this limitation, a feature significance-based multi-BOVW scene classification method was proposed, which integrates the information of feature separating capabilities among different scene categories into the traditional two-phase classification-based score-level fusion framework, realizing different treatments for different feature channels in classifying different scene categories. Experimental results show that the proposed method outperforms the traditional score-level fusion-based multi-BOVW methods and effectively explores the feature significance information in multiclass remote sensing image scene classification tasks.
Land-cover composition and change are important factors that affect global ecosystem. As an effective means for Earth
observation, remote sensing technique has been widely applied in extracting land-cover information and in monitoring
land-use and land-cover change, among which image classification becomes a key issue. Most existing studies about
object-oriented classification use traditional low-level feature extraction methods or statistics of low-level features to
represent objects in an image, which, to a large extent, loses the information in remote sensing images. Therefore, in
order to facilitate better description of these objects in object-oriented classification, this paper introduces a state-of-theart
feature representation method called bag-of-visual-words (BOVW) to construct the middle-level representations
instead of low-level features. Based on the idea of BOVW, this paper proposes a BOVW based framework for objectoriented
land-cover classification. For a given remote sensing image, it first applies a pixel-level local feature extraction
strategy to construct a visual vocabulary by K-means clustering with each cluster as a visual word. Then the image is
segmented into objects and each object is represented as a histogram of visual word occurrences by mapping the local
pixel-level features in this object to the learned visual words. Finally, the calculated histogram is considered as the final
representation of an object which can be used for further classification tasks. Experimental results on a SPOT5 satellite
image, acquired from the Changping County in Beijing, China, in 2002, show that the proposed method is superior to the
traditional low-level feature based method in classification accuracy by about 2%.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.