Abstract
Locality-constrained Linear Coding (LLC) shows superior image classification performance due to its underlying properties of local smooth sparsity and good construction. It encodes the visual features in remote sensing images and realizes the process of modeling human visual perception of an image through a computer. However, it ignores the consideration of saliency preprocessing in the human visual system. Saliency detection preprocessing can effectively enhance a computer’s perception of remote sensing images. To better implement the task of remote sensing image scene classification, this paper proposes a new approach by combining saliency detection preprocessing and LLC. This saliency detection preprocessing approach is realized using spatial pyramid Gaussian kernel density estimation. Experiments show that the proposed method achieved a better performance for remote sensing scene classification tasks.
Highlights
Over recent decades, an overwhelming amount of high-resolution (HR) remote sensing images have become available
The main contribution of this paper is to propose a new kind of remote sensing scene classification improved
There are some unsatisfactory saliency maps for images that may lead to classification confusion; for instance, the pond scene image in Figure 5, for images that may lead to classification confusion; for instance, the pond scene image in Figure 5, where the features of the pond are vague, whereas the bridge becomes rather prominent after where the features of the pond are vague, whereas the bridge becomes rather prominent after saliency saliency detection preprocessing and would be wrongly classified into the park scene class, though detection preprocessing and would be wrongly classified into the park scene class, though this this phenomenon is very rare
Summary
An overwhelming amount of high-resolution (HR) remote sensing images have become available. Various methods have been proposed to classify remote sensing scenes over the years. Bag-of-Features (BoF) [3,4] is a classical method in whole-image categorization tasks. This method first forms a histogram based on a remote sensing image’s local features, and uses the histogram to represent the remote sensing image. This method lacks consideration of the spatial layout information of features in remote sensing images. The SPM method divides a remote sensing image into different scale spatial sub-regions. Histograms of local features from each sub-region are computed
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