Hyperspectral remote sensing images have been shown to be particularly beneficial for detecting the types of materials in a scene due to their unique spectral properties. This paper proposes a novel semantic segmentation method for hyperspectral image (HSI), which is based on a new spatial-spectral filtering, called extended extrema morphological profiles (EEMPs). Firstly, principal component analysis (PCA) is used as the feature extractor to construct the feature maps by extracting the first informative feature from the hyperspectral image (HSI). Secondly, the extrema morphological profiles (EMPs) are used to extract the spatial-spectral feature from the informative feature maps to construct the EEMPs. Finally, support vector machine (SVM) is utilized to obtain accurate semantic segmentation from the EEMPs. In order to evaluate the semantic segmentation results, the proposed method is tested on a widely used hyperspectral dataset, i.e., Houston dataset, and four metrics, i.e., class accuracy (CA), overall accuracy (OA), average accuracy (AA), and Kappa coefficient, are used to quantitatively measure the segmentation accuracy. The experimental results demonstrate that EEMPs can efficiently achieve good semantic segmentation accuracy.
Image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.
In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation.
First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose
normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To
obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along
the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension
distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if
yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them
together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the
standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor
achieves the best performance on feature matching experiments.
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