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Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery

Authors :
Chandra Shekhar Yadav
Monoj Kumar Pradhan
Syam Machinathu Parambil Gangadharan
Jitendra Kumar Chaudhary
Jagendra Singh
Arfat Ahmad Khan
Mohd Anul Haq
Ahmed Alhussen
Chitapong Wechtaisong
Hazra Imran
Zamil S. Alzamil
Himansu Sekhar Pattanayak
Source :
Electronics; Volume 11; Issue 17; Pages: 2799
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient.

Details

ISSN :
20799292
Volume :
11
Database :
OpenAIRE
Journal :
Electronics
Accession number :
edsair.doi.dedup.....f46fa8842fda1c96ba5f8138f20ffd50
Full Text :
https://doi.org/10.3390/electronics11172799