Water body extraction plays an important role in flood control and the utilization of water resources. With the launch of China’s first high-resolution (50m) geostationary optical GF-4 satellite at the end of December 2015, the wide-swath (400km) and high-frequency (up to minutes) imaging capabilities have been greatly improved, which provides new possibilities for rapid and accurate water body monitoring. To explore the potential of GF-4 satellite in water body monitoring, this paper proposes a water body extraction method based on the temporal variability of near infrared (NIR) spectral features. For a series of preprocessed and coregistered GF-4 images, one of them is chosen as the base image whose NIR band (B5) thresholding is firstly applied to eliminate most of the non-water regions. Then, for each pixel, the variance of B5 radiance values of all images is calculated to obtain a variogram, and pixels whose variogram values are larger than a certain threshold given by the OTSU algorithm are further eliminated. Finally, the final water body extraction result can be obtained after post-classification processing. To evaluate the efficacy of the proposed method, two groups of GF-4 datasets with complex water distribution are selected in the areas of the middle and lower reaches of Yangtze River in China. Experimental results demonstrate that thanks to the high-frequency and high-resolution characteristics of GF-4, the proposed method can extract more tiny waters and effectively remove built-up areas, and is superior to the extraction accuracy of water index way by about 4%.
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%.
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