4.7 Article

Urban Feature Extraction within a Complex Urban Area with an Improved 3D-CNN Using Airborne Hyperspectral Data

Related references

Note: Only part of the references are listed.
Article Environmental Sciences

Small Sample Hyperspectral Image Classification Based on Cascade Fusion of Mixed Spatial-Spectral Features and Second-Order Pooling

Fan Feng et al.

Summary: This paper proposes a novel mixed spatial-spectral features cascade fusion network (MSSFN) for small sample hyperspectral classification. The network models the covariance structure of hyperspectral data, conducts dimensionality reduction, and extracts mixed spatial-spectral features using residual modules. A cascade fusion pattern is employed to enhance feature extraction capability. Experimental results show that MSSFN achieves high accuracy in small sample hyperspectral classification tasks.

REMOTE SENSING (2022)

Article Environmental Sciences

Hyperspectral Image Classification Based on 3D Coordination Attention Mechanism Network

Cuiping Shi et al.

Summary: In recent years, the deep learning method has been widely used in hyperspectral image classification tasks. However, the features extracted by classical deep learning methods have limited discrimination ability, and the limited data samples of hyperspectral images pose challenges for achieving high classification performance. To address these issues, this paper proposes a deep learning network framework called 3DCAMNet, which incorporates a three-dimensional coordination attention mechanism to extract spectral and spatial information more effectively. Experimental results demonstrate that 3DCAMNet outperforms state-of-the-art methods in terms of classification performance and robustness.

REMOTE SENSING (2022)

Article Environmental Sciences

Attention Mechanism and Depthwise Separable Convolution Aided 3DCNN for Hyperspectral Remote Sensing Image Classification

Wenmei Li et al.

Summary: This article introduces a solution to the classification of hyperspectral remote sensing images by introducing an attention mechanism and depthwise separable convolution to a three-dimensional convolutional neural network. The proposed models, 3DCNN-AM and 3DCNN-AM-DSC, have been shown to improve classification accuracy and reduce computing time.

REMOTE SENSING (2022)

Article Environmental Sciences

Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification

Tianxiang Zhang et al.

Summary: This study proposes a dual-flow architecture named Hyper-LGNet, which integrates CNN and Transformer branches to handle spatial-spectral information in HSI. Experimental results show that the proposed method achieves state-of-the-art performance in terms of accuracy and other metrics.

REMOTE SENSING (2022)

Article Computer Science, Information Systems

Average biased ReLU based CNN descriptor for improved face retrieval

Shiv Ram Dubey et al.

Summary: This paper proposes using AB-ReLU to improve the discriminative ability of deep image representation models, achieving superior performance in face retrieval tasks.

MULTIMEDIA TOOLS AND APPLICATIONS (2021)

Article Environmental Sciences

A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image Classification

Dongxu Liu et al.

Summary: This study presents a novel 2D-3D CNN with spectral-spatial multi-scale feature fusion for hyperspectral image classification, leveraging two feature extraction streams and a classification scheme to achieve higher accuracy. Experimental results demonstrate its superiority over existing methods.

REMOTE SENSING (2021)

Article Computer Science, Hardware & Architecture

Automated building and road classifications from hyperspectral imagery through a fully convolutional network and support vector machine

R. Tamilarasi et al.

Summary: Hyperspectral imagery is useful for determining urban-related characteristics such as roads, trees, buildings, and structures. Researchers are currently focusing on deep learning and machine learning methods for image classification. This research proposes a new technique for dimensionality reduction and classification, combining ICA, PCA, FCN, and SVM models, to extract road and building features with high accuracy from hyperspectral images. Experimental results show better accuracy compared to existing machine learning approaches.

JOURNAL OF SUPERCOMPUTING (2021)

Article Geochemistry & Geophysics

Semisupervised Classification Based on SLIC Segmentation for Hyperspectral Image

Yuxiang Zhang et al.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2020)

Article Environmental Sciences

Classification of Urban Area Using Multispectral Indices for Urban Planning

Philip Lynch et al.

REMOTE SENSING (2020)

Article Computer Science, Information Systems

Cloud and Cloud Shadow Detection Based on Multiscale 3D-CNN for High Resolution Multispectral Imagery

Yang Chen et al.

IEEE ACCESS (2020)

Article Geochemistry & Geophysics

Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning

Haokui Zhang et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2019)

Article Engineering, Electrical & Electronic

Spectral-Spatial Feature Extraction for HSI Classification Based on Supervised Hypergraph and Sample Expanded CNN

Yi Kong et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2018)

Article Engineering, Electrical & Electronic

Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensed Scene

Renbo Luo et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2017)

Article Environmental Sciences

Urban Shadow Detection and Classification Using Hyperspectral Image

Xiaojun Qiao et al.

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING (2017)

Article Computer Science, Information Systems

Integrating Geospatial Techniques for Urban Land Use Classification in the Developing Sub-Saharan African City of Lusaka, Zambia

Matamyo Simwanda et al.

ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION (2017)

Article Geochemistry & Geophysics

Hyperspectral Feature Extraction Using Total Variation Component Analysis

Behnood Rasti et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)

Article Geochemistry & Geophysics

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Yushi Chen et al.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2016)

Article Geography, Physical

Mapping of land cover in northern California with simulated hyperspectral satellite imagery

Matthew L. Clark et al.

ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING (2016)

Proceedings Paper Computer Science, Artificial Intelligence

Learning Spatiotemporal Features with 3D Convolutional Networks

Du Tran et al.

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2015)

Article Computer Science, Artificial Intelligence

3D Convolutional Neural Networks for Human Action Recognition

Shuiwang Ji et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013)

Article Computer Science, Artificial Intelligence

A comparison of methods for multiclass support vector machines

CW Hsu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS (2002)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)