Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels

By Leyuan Fang, Shutao Li, Wuhui Duan, Jinchang Ren, Jon Atli Benediktsson. IEEE Transactions on Geoscience and Remote Sensing, 2015

354 citations recorded by the source corpus.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/d80e7c2280caf016404ffa02469b331e755cfc7b

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. Shutao Li
2. Pedram Ghamisi
3. Jun Yu Li
4. Leyuan Fang
5. Jon Atli Benediktsson
6. Yushi Chen
7. Weiwei Song
8. Lin He

In Media Technology

1. Shutao Li
2. Leyuan Fang
3. Pedram Ghamisi
4. Jon Atli Benediktsson
5. Yushi Chen
6. Jun Yu Li
7. Weiwei Song
8. Lin He

Citations in the corpus

• Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, methodology): “filtering is used to characterize the adaptive spatial information [78], [101].”
• Hyperspectral Image Restoration Via Total Variation Regularized Low-Rank Tensor Decomposition (2017, methodology): “, the works [23]–[25] for HSI classification task and the works [26] and[27] for band selection task.”
• Hyperspectral Image Classification With Deep Feature Fusion Network (2018, background): “, composite kernel [12] and morphological kernel [13], [14]) based on spectral–spatial information is designed to improve the SVM classifier.”
• Deep Learning for Hyperspectral Image Classification: An Overview (2019, background): “nel [22], [23]) was designed to explore the spectral–spatial information of HSIs.”
• Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network (2019, methodology): “Based on SVM, the morphological kernel [16] and the composite kernel [17] methods were also proposed to exploit spectral-spatial information.”