Novel object-based filter for improving land-cover classification of aerial imagery with very high spatial resolution

Zhiyong Lv, Wen Zhong Shi, Jón Atli Benediktsson, Xiaojuan Ning

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

Land cover classification using very high spatial resolution (VHSR) imaging plays a very important role in remote sensing applications. However, image noise usually reduces the classification accuracy of VHSR images. Image spatial filters have been recently adopted to improve VHSR image land cover classification. In this study, a new object-based image filter using topology and feature constraints is proposed, where an object is considered as a central object and has irregular shapes and various numbers of neighbors depending on the nature of the surroundings. First, multi-scale segmentation is used to generate a homogeneous image object and extract the corresponding vectors. Then, topology and feature constraints are proposed to select the adjacent objects, which present similar materials to the central object. Third, the feature of the central object is smoothed by the average of the selected objects' feature. This proposed approach is validated on three VHSR images, ranging from a fixed-wing aerial image to UAV images. The performance of the proposed approach is compared to a standard object-based approach (OO), object correlative index (OCI) spatial feature based method, a recursive filter (RF), and a rolling guided filter (RGF), and has shown a 6%-18% improvement in overall accuracy.
Original languageEnglish
Article number1023
JournalRemote Sensing
Volume8
Issue number12
DOIs
Publication statusPublished - 1 Dec 2016

Keywords

  • Image filter
  • Land cover classification
  • Multi-scale segmentation
  • Very high spatial resolution (VHSR) aerial image

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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