High-Dimensional Multiresolution Satellite Image Classification: An Approach Blending the Advantages of Convex Optimization and Deep Learning

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

To protect valuable mangrove ecosystems, efficient and accurate mangrove area mapping becomes essential, for which high-dimensional multiresolution satellite image classification is the critical technique. The previous index-based methods only consider spectral information, and perform classification pixel-by-pixel ignoring the spatial continuity nature of the mangrove distribution. We introduce convex optimization (CO) into deep learning (DL) to achieve outstanding classification performance, without relying on big data or math-heavy regularization. Based on a rough mangrove multispectral signature estimated by mangrove vegetation index (MVI), but ruling out its key disadvantage of pixel-independent estimation in MVI via DL, our method introduces a deep regularizer employing pixel-dependence into a CO framework. The proposed classification method, termed MSMCA, is applied to mangrove mapping, showing state-of-the-art classification performance.

Original languageEnglish
Title of host publication2022 12th Workshop on Hyperspectral Imaging and Signal Processing
Subtitle of host publicationEvolution in Remote Sensing, WHISPERS 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665470698
DOIs
Publication statusPublished - 2022
Event12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022 - Rome, Italy
Duration: 2022 Sept 132022 Sept 16

Publication series

NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume2022-September
ISSN (Print)2158-6276

Conference

Conference12th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2022
Country/TerritoryItaly
CityRome
Period22-09-1322-09-16

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing

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