Spectral-Spatial Response for Hyperspectral Image Classification
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Summary
This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboring regions and proposes the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification.
- Type
- article
- Published
- 2017-02-24
- Cited by
- 25
- References
- 75
- Access
- Open access
- OpenAlex
- https://openalex.org/W2589232018
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:2950207
Keywords
Hyperspectral imaging, Computer science, Imaging spectrometer, Principal component analysis, Artificial intelligence
References
- PCANet: A Simple Deep Learning Baseline for Image Classification?
- Hyperspectral image classification via contextual deep learning
- Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
- Classification of Hyperspectral Images by Exploiting Spectral–Spatial Information of Superpixel via Multiple Kernels
- Spectral–spatial classification of hyperspectral images using deep convolutional neural networks
- Semisupervised Discriminative Locally Enhanced Alignment for Hyperspectral Image Classification
- Spectral-Spatial Classification of Hyperspectral Image Based on Kernel Extreme Learning Machine
- Supervised Spectral–Spatial Hyperspectral Image Classification With Weighted Markov Random Fields
- Semi-supervised multiview embedding for hyperspectral data classification
- Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding
- Automatic Generation of Standard Deviation Attribute Profiles for Spectral–Spatial Classification of Remote Sensing Data
- Hierarchical Feature Extraction With Local Neural Response for Image Recognition
- Discriminant Tensor Spectral–Spatial Feature Extraction for Hyperspectral Image Classification
- Extreme learning machines: a survey
- A Novel Method for Hyperspectral Image Classification Based on Laplacian Eigenmap Pixels Distribution-Flow
- Learning and generalization characteristics of the random vector Functional-link net
- Generalized Composite Kernel Framework for Hyperspectral Image Classification
- Advances in Spectral-Spatial Classification of Hyperspectral Images
- Spectral-Spatial Constraint Hyperspectral Image Classification
- Hierarchical kernel-based rotation and scale invariant similarity
Cited by
- Dimensionality-varied deep convolutional neural network for spectral–spatial classification of hyperspectral data
- A Robust Method for Estimating Image Geometry With Local Structure Constraint
- A non-parametric depth modification model for registration between color and depth images
- Spectral-Spatial Feature Extraction of Hyperspectral Images Based on Propagation Filter
- 3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification
- Prior Knowledge-Based Probabilistic Collaborative Representation for Visual Recognition
- Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review
- Unsupervised Dimensionality Reduction for Hyperspectral Imagery via Local Geometric Structure Feature Learning
- Deep Feature Fusion via Two-Stream Convolutional Neural Network for Hyperspectral Image Classification
- Latent Relationship Guided Stacked Sparse Autoencoder for Hyperspectral Imagery Classification
- Generalized Visual Information Analysis Via Tensorial Algebra
- Multiple geometry atmospheric correction for image spectroscopy using deep learning
- Regional Principal Component Analysis Network With the Rolling Guidance Filter for Classifying the Hyperspectral Images
- Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification
- Classification of Hyperspectral Images based on Intrinsic Image Decomposition and Deep Convolutional Neural Network
- A Sparse Oblique-Manifold Nonnegative Matrix Factorization for Hyperspectral Unmixing
- Spatial-Aware Network for Hyperspectral Image Classification
- Locally Homogeneous Covariance Matrix Representation for Hyperspectral Image Classification
- Improved artificial bee colony algorithm and its application in image threshold segmentation
- Hierarchical broad learning system for hyperspectral image classification
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