In this paper, a novel linear supervised dimensionality reduction algorithm, called Locality and Global Geometric Structure Preserving (LGGSP) projection, is ...
Local structure has shown great efficiency in feature extraction. Yet recent progress has also demonstrated the importance of global geometric structure in ...
We evaluate our proposed method with PCA [15], LapLDA. [8], MFA [16,17], LPP [18], RP [2], LGSPP [19], and JGLDA. [10] for hyperspectral image classification.
Incorporating local and global geometric structure for hyperspectral image classification. H. Luo, Y. Tang, and L. Yang. SMC, page 4092-4096. IEEE, (2014 ).
We propose a class feature fused fully convolutional network (CFF-FCN) with a local feature extraction block (LFEB) and a class feature fusion block (CFFB)
Apr 15, 2015 · In this paper, a novel linear supervised dimensionality reduction algorithm, called Locality and Global Geometric Structure Preserving (LGGSP) projection, is ...
Missing: Incorporating | Show results with:Incorporating
Graph based semi-supervised learning provides an effective solution to model data in classification problems, of which graph construction is the critical step.
To tackle this issue, this article introduces an HSI classification method, based on multi-scale convolutional features and multi-attention mechanisms (i.e., ...
Jun 12, 2024 · Color histograms, GIST, and texture descriptors are global features, capturing statistical characteristics, while HOG and SIFT are local ...
They help reduce huge dimensions, seek global and local-spatial features, and optimize the KELM parameters to obtain the class labels [166]. A variant of ...