Classification of cloudy hyperspectral image and LiDAR data based on feature fusion and decision fusion

2016 ◽  
Author(s):  
Renbo Luo ◽  
Wenzhi Liao ◽  
Hongyan Zhang ◽  
Youguo Pi ◽  
Wilfried Philips
Keyword(s):  
Hyperspectral Image ◽  
Feature Fusion ◽  
Decision Fusion ◽  
Lidar Data

scholarly journals Combining feature fusion and decision fusion for classification of hyperspectral and LiDAR data

2014 ◽  
Author(s):  
Wenzhi Liao ◽  
Rik Bellens ◽  
Aleksandra Pizurica ◽  
Sidharta Gautama ◽  
Wilfried Philips
Keyword(s):  
Feature Fusion ◽  
Decision Fusion ◽  
Lidar Data

Development of Acoustical Feature Based Classifier Using Decision Fusion Technique for Malay Language Disfluencies Classification

2017 ◽  
Vol 8 (1) ◽  
pp. 262
Author(s):  
Raseeda Hamzah ◽  
Nursuriati Jamil ◽  
Rosniza Roslan
Keyword(s):  
Feature Fusion ◽  
Decision Fusion ◽  
Classification Performance ◽  
Zero Crossing ◽  
Single Feature ◽  
Automated Speech Recognition ◽  
Feature Based ◽  
Speech Envelope ◽  
Speech Disfluency

<p>Speech disfluency such as filled pause (FP) is a hindrance in Automated Speech Recognition as it degrades the accuracy performance. Previous work of FP detection and classification have fused a number of acoustical features as fusion classification is known to improve classification results. This paper presents new decision fusion of two well-established acoustical features that are zero crossing rates (ZCR) and speech envelope (ENV) with eight popular acoustical features for classification of Malay language filled pause (FP) and elongation (ELO). Five hundred ELO and 500 FP are selected from a spontaneous speeches of a parliamentary session and Naïve Bayes classifier is used for the decision fusion classification. The proposed feature fusion produced better classification performance compared to single feature classification with the highest F-measure of 82% for both classes.</p>

scholarly journals Hyperspectral Image Denoising and Classification Using Multi-Scale Weighted EMAPs and Extreme Learning Machine

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2137
Author(s):  
Meizhuang Liu ◽  
Faxian Cao ◽  
Zhijing Yang ◽  
Xiaobin Hong ◽  
Yuezhen Huang
Keyword(s):  
Extreme Learning Machine ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Decision Fusion ◽  
Joint Decision ◽  
Multiple Attribute ◽  
Mean Filter ◽  
Learning Machine ◽  
Elm Classifier ◽  
Homogeneous Regions

Recently, extended multi-attribute profiles (EMAPs) have attracted much attention due to its good performance while applied to remote sensing images feature extraction and classification. Since the EMAPs connect multiple attribute features without considering the pixel-based Hyperspectral Image (HSI) classification, homogeneous regions may become unsmooth due to the noise to be introduced. To tackle this problem, we propose the weighted EMAPs (WEMAPs) to reduce the noise and smoothen the homogeneous regions based on weighted mean filter (WMF). Then, we construct multiscale WEMAPs to product multiscale feature in order to extract different spatial structures of the HSI and produce better classification results. Finally, a new joint decision fusion and feature fusion (JDFFF) framework is proposed based on the decision fusion (DF) and the multiscale WEMAPs (MWEMAPs) based on extreme learning machine (ELM) classifier. That is, the classification results from various scales are combined into a final one with ELM to perform the HSI classification. Experiment results show that the proposed algorithm significantly outperforms many state-of-the-art HSI classification algorithms.

Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion

2017 ◽  
Vol 8 (10) ◽  
pp. 957-966 ◽  
Author(s):  
Mengmeng Zhang ◽  
Pedram Ghamisi ◽  
Wei Li
Keyword(s):  
Feature Fusion ◽  
Lidar Data

scholarly journals A New Methodology for Spectral-Spatial Classification of Hyperspectral Images

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Zelang Miao ◽  
Wenzhong Shi
Keyword(s):  
Hyperspectral Image ◽  
Fusion Rule ◽  
Decision Fusion ◽  
Hyperspectral Images ◽  
Selection Strategies ◽  
Recent Developments ◽  
Spatial Method ◽  
Validation Tests ◽  
Statistical Region Merging

Recent developments in hyperspectral images have heightened the need for advanced classification methods. To reach this goal, this paper proposed an improved spectral-spatial method for hyperspectral image classification. The proposed method mainly consists of three steps. First, four band selection strategies are proposed to utilize the statistical region merging (SRM) method to segment the hyperspectral image. The segmentation map is subsequently integrated with the pixel-wise classification method to classify the hyperspectral image. Finally, the final classification result is obtained using the decision fusion rule. Validation tests are performed to evaluate the performance of the proposed approach, and the results indicate that the new proposed approach outperforms the state-of-the-art methods.

scholarly journals Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification

2021 ◽  
Vol 13 (6) ◽  
pp. 1143
Author(s):  
Yinghui Quan ◽  
Yingping Tong ◽  
Wei Feng ◽  
Gabriel Dauphin ◽  
Wenjiang Huang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Total Variation ◽  
Structure Analysis ◽  
Hyperspectral Image ◽  
Gabor Filter ◽  
Heterogeneous Data ◽  
Lidar Data ◽  
Fusion Method ◽  
Wide Range ◽  
Relative Total Variation

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.

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