A Kernel-Based Extreme Learning Machine Framework for Classification of Hyperspectral Images Using Active Learning

Pradhan, Monoj K.; Minz, Sonajharia; Shrivastava, Vimal K.

doi:10.1007/s12524-019-01021-6

Cited by 7 publications

(1 citation statement)

References 37 publications

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“…After the experiments and results, the authors concluded with a balance between development and destruction. Other application-based research in the remote sensing area has been carried out by the authors cited from [22][23][24][25][26][27][28][29]. In [30], Jin, S. et al proposed the CRMC (Combined Reflectance simulation and Machine learning for Cloud detection) method, based on radiative transfer simulation and machine learning for detecting cloud pixels in the optical image of the FY-3D satellite MERSI II sensor.…”

Section: Related Workmentioning

confidence: 99%

Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods

et al. 2022

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Remote sensing technology has penetrated all the natural resource segments as it provides precise information in an image mode. Remote sensing satellites are currently the fastest-growing source of geographic area information. With the continuous change in the earth’s surface and the wide application of remote sensing, change detection is very useful for monitoring environmental and human needs. So, it is necessary to develop automatic change detection techniques to improve the quality and reduce the time required by manual image analysis. This work focuses on the improvement of the classification accuracy of the machine learning techniques by reviewing the training samples and comparing the post-classification comparison with the image differencing in the algebraic technique. Landsat data are medium spatial resolution data; that is why pixel-wise computation has been applied. Two change detection techniques have been studied by applying a decision tree algorithm using a separability matrix and image differencing. The first change detection, e.g., the separability matrix, is a post-classification comparison in which individual images are classified by a decision tree algorithm. The second change detection is, e.g., the image differencing change detection technique in which changed and unchanged pixels are determined by applying the corner method to calculate the threshold on the changing image. The performance of the machine learning algorithm has been validated by 10-fold cross-validation. The experimental results show that the change detection using the post-classification method produced better results when compared to the image differencing of the algebraic change detection technique.

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