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Crops Fine Classification in Airborne Hyperspectral Imagery Based on Multi-Feature Fusion and Deep Learning
- Source :
- Remote Sensing, Volume 13, Issue 15, Pages: 2917, Remote Sensing, Vol 13, Iss 2917, p 2917 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Hyperspectral imagery has been widely used in precision agriculture due to its rich spectral characteristics. With the rapid development of remote sensing technology, the airborne hyperspectral imagery shows detailed spatial information and temporal flexibility, which open a new way to accurate agricultural monitoring. To extract crop types from the airborne hyperspectral images, we propose a fine classification method based on multi-feature fusion and deep learning. In this research, the morphological profiles, GLCM texture and endmember abundance features are leveraged to exploit the spatial information of the hyperspectral imagery. Then, the multiple spatial information is fused with the original spectral information to generate classification result by using the deep neural network with conditional random field (DNN+CRF) model. Specifically, the deep neural network (DNN) is a deep recognition model which can extract depth features and mine the potential information of data. As a discriminant model, conditional random field (CRF) considers both spatial and contextual information to reduce the misclassification noises while keeping the object boundaries. Moreover, three multiple feature fusion approaches, namely feature stacking, decision fusion and probability fusion, are taken into account. In the experiments, two airborne hyperspectral remote sensing datasets (Honghu dataset and Xiong’an dataset) are used. The experimental results show that the classification performance of the proposed method is satisfactory, where the salt and pepper noise is decreased, and the boundary of the ground object is preserved.
- Subjects :
- Conditional random field
Endmember
010504 meteorology & atmospheric sciences
Computer science
Science
0211 other engineering and technologies
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
conditional random field
01 natural sciences
Spatial analysis
021101 geological & geomatics engineering
0105 earth and related environmental sciences
multi-feature fusion
Artificial neural network
business.industry
Deep learning
deep neural network
Hyperspectral imaging
Pattern recognition
crops fine classification
ComputingMethodologies_PATTERNRECOGNITION
hyperspectral imagery
Feature (computer vision)
General Earth and Planetary Sciences
Precision agriculture
Artificial intelligence
business
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Database :
- OpenAIRE
- Journal :
- Remote Sensing
- Accession number :
- edsair.doi.dedup.....3b748de6a3570b68d60f2eb062e9fdd7
- Full Text :
- https://doi.org/10.3390/rs13152917