Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification

By Li Ma, Melba M. Crawford, Jinwen Tian. IEEE Transactions on Geoscience and Remote Sensing, 2010

547 citations recorded by the source corpus.

Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/c86abfc2d6c5ad3ed80b133ea736529301f2e500

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Qian Du
2. Wei Li
3. Gong Cheng
4. Junwei Han
5. Nasser M. Nasrabadi
6. Yi Chen
7. Trac Duy Tran
8. Yuan Yuan

In Computer Vision and Pattern Recognition

1. Junwei Han
2. Gong Cheng
3. Xiaoqiang Lu
4. Qian Du
5. Xuelong Li
6. Yuan Yuan
7. Nasser M. Nasrabadi
8. Wei Li

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Qian Du
4. Xiaoqiang Lu
5. Xuelong Li
6. Yuan Yuan
7. Wei Li
8. Nasser M. Nasrabadi

Citations in the corpus

• Collaborative Representation for Hyperspectral Anomaly Detection (2015, methodology): “The second data were also collected by HYDICE airborne sensor [21].”
• Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014, unknown): “Unsupervised local manifold-learning approaches search the k spectral neighbors of a given point, whereas supervised local manifold-learning approaches identify only the neighbors that are of the same class as the given point, often making these methods more attractive for classification [26], [27].”
• Feature Mining for Hyperspectral Image Classification (2013, background): “[39] S. Kumar, J. Ghosh, and M. M. Crawford, ‘‘Best-bases feature extraction algorithms for classification of hyperspectral data,’’ IEEE Trans.”
• Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013, background): “This assumption is referred to as manifold assumption, which can be applied in several image processing fields such as hyperspectral image classification [28], hyperspectral imagery representation [29], and manifold denoising [30].”
• ReDMark: Framework for residual diffusion watermarking based on deep networks (2018, methodology): “Nevertheless, most of them generally utilize machine learning tools such as Support Vector Machine (SVM) [18], Support Vector Regression (SVR) [19], Radial Basic Function Neural Network (RBFNN) [20], and K Nearest Neighbor (KNN) [21] for specific parts of watermarking procedure such as parameter opt”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, methodology): “It is one of the most simple and traditional classification tools and has been extensively used for geospatial object detection and image classification (Blanzieri and Melgani, 2008; Cheng et al., 2013a; Haapanen et al., 2004; Ma et al., 2010; Yang et al., 2010; Zhu and Basir, 2005).”
• Hyperspectral image classification via kernel sparse representation (2011, methodology): “In [15], the k-nearestneighbor classifier is applied to the local manifolds to exploit the intrinsic nonlinear structure of hyperspectral images.”