Jan 28, 2017 · The proposed SSKNN method extends the traditional KNN algorithm in a spectral-spatial collaborative manner, which effectively integrates the set ...
In the proposed SSKNN framework, a set-to-point distance is exploited based on least squares and a weighted KNN method is used to achieve stable performance. By ...
Nov 12, 2020 · This method introduced the homogeneity order index and applied the K-nearest neighbors in feature space for robust spectral-spatial markers ...
Nov 15, 2024 · We combine k-nearest neighbor (KNN) algorithm with guided filter which can extract spatial context information and denoise the classification ...
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A Spectral–Spatial Approach for Hyperspectral Image Classification ...
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Aug 26, 2014 · This paper proposes a novel approach to classify hyperspectral (HS) images using both spectral and spatial information.
We combine k-nearest neighbor algorithm with guided filter which can extract spatial context information and denoise the classification results by edge- ...
Fusion of spectral and spatial information is an effective way in improving the accuracy of hyperspectral image classification.
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In this paper, we combine k-nearest neighbor with guided filter to mine spatial information effectively or and optimize the classification accuracy.
To be specific, we propose a spectral-spatial KNN (SSKNN) method to deal with the HSI classification problem, which effectively exploits the distances all ...
ECA will consider each channel and its k nearest neighbors to complete the computation of channel weights by fast 1D convolution. k is determined adaptively by ...