Unsupervised classification of hyperspectral-image data using fuzzy approaches that spatially exploit membership relations

The analysis, measurement, and computation of remote sensing images often require enhanced unsupervised/supervised classification approaches. The goal of this study is to have a better understanding of (a) the classification performance of multispectral image and hyperspectral image data; (b) the classification performance of unsupervised and supervised models; and (c) the classification performance of feature selection among different models. More specifically, the multispectral images and hyperspectral images with high spatial resolution are well accepted for improving land use and classification. Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN), linear discriminant analysis (LDA), and back-propagation neural network (BPN). Then the feature selection (principle component analysis, PCA) on four classifiers is studied. The results show that the image material of CASI-1500 classification accuracy is slightly better than that of WorldView-2. The overall classification of BPN is the best, the overall data has a κ value of 0.89 and the overall accuracy is 97%. The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time.

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Dynamic descriptors for contextual classification of remotely sensed hyperspectral image data analysis

Applied Optics ◽

10.1364/ao.23.003889 ◽

1984 ◽

Vol 23 (21) ◽

pp. 3889 ◽

Cited By ~ 1

Author(s):

Wun C. Chiou

Keyword(s):

Data Analysis ◽

Hyperspectral Image ◽

Image Data ◽

Remotely Sensed ◽

Hyperspectral Image Data

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An Innovative Intelligent System with Integrated CNN and SVM: Considering Various Crops through Hyperspectral Image Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040242 ◽

2021 ◽

Vol 10 (4) ◽

pp. 242

Author(s):

Shiuan Wan ◽

Mei Ling Yeh ◽

Hong Lin Ma

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Image Classification ◽

Hyperspectral Image ◽

Intelligent System ◽

Image Data ◽

Support Vector ◽

Hyperspectral Image Classification ◽

Hyperspectral Image Data

Generation of a thematic map is important for scientists and agriculture engineers in analyzing different crops in a given field. Remote sensing data are well-accepted for image classification on a vast area of crop investigation. However, most of the research has currently focused on the classification of pixel-based image data for analysis. The study was carried out to develop a multi-category crop hyperspectral image classification system to identify the major crops in the Chiayi Golden Corridor. The hyperspectral image data from CASI (Compact Airborne Spectrographic Imager) were used as the experimental data in this study. A two-stage classification was designed to display the performance of the image classification. More specifically, the study used a multi-class classification by support vector machine (SVM) + convolutional neural network (CNN) for image classification analysis. SVM is a supervised learning model that analyzes data used for classification. CNN is a class of deep neural networks that is applied to analyzing visual imagery. The image classification comparison was made among four crops (paddy rice, potatoes, cabbages, and peanuts), roads, and structures for classification. In the first stage, the support vector machine handled the hyperspectral image classification through pixel-based analysis. Then, the convolution neural network improved the classification of image details through various blocks (cells) of segmentation in the second stage. A series of discussion and analyses of the results are presented. The repair module was also designed to link the usage of CNN and SVM to remove the classification errors.

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