A Semi-Supervised Deep Rule-Based Approach for Complex Satellite Sensor Image Analysis

Xiaowei Gu, Plamen Angelov, Ce Zhang, Peter M. Atkinson

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

6 Dyfyniadau(SciVal)
80 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

Large-scale {(large-area)}, fine spatial resolution satellite sensor images are valuable data sources for Earth observation while not yet fully exploited by research communities for practical applications. Often, such images exhibit highly complex geometrical structures and spatial patterns, and distinctive characteristics of multiple land-use categories may appear at the same region. Autonomous information extraction from these images is essential in the field of pattern recognition within remote sensing, but this task is extremely challenging due to the spectral and spatial complexity captured in satellite sensor imagery. In this research, a semi-supervised deep rule-based approach for satellite sensor image analysis (SeRBIA) is proposed, where large-scale satellite sensor images are analysed autonomously and classified into detailed land-use categories. Using an ensemble feature descriptor derived from pre-trained AlexNet and VGG-VD-16 models, SeRBIA is capable of learning continuously from both labelled and unlabelled images through self-adaptation without human involvement or intervention. Extensive numerical experiments were conducted on both benchmark datasets and real-world satellite sensor images to comprehensively test the validity and effectiveness of the proposed method. The novel information mining technique developed here can be applied to analyse large-scale satellite sensor images with high accuracy and interpretability, across a wide range of real-world applications.
Iaith wreiddiolSaesneg
Tudalennau (o-i)2281-2292
Nifer y tudalennau12
CyfnodolynIEEE Transactions on Pattern Analysis and Machine Intelligence
Cyfrol44
Rhif cyhoeddi5
Dyddiad ar-lein cynnar30 Rhag 2020
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 01 Mai 2022

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