Adaptive region-based post-classification framework for land-cover mapping improvement using very high spatial resolution optical imagery

Zhiyong Lv, Guangfei Li, Jixing Yan, Jón Atli Benediktsson

Research output: Contribution to journalArticlepeer-review

Abstract

Land cover mapping using remote sensing optical images with a very high spatial resolution (VHSR) plays an important role in observing the Earth's surface. However, classification maps are usually affected by salt-And-pepper noise because VHSR optical images usually have a low resolution of ground targets in terms of spectral reflectance. An adaptive region-based post-classification framework (ARPF) is proposed for improving the initial classification map while using a VHSR optical image to further smooth the noise of initial classified maps. First, different from several traditional methods using a single classifier, our proposed ARPF needs more than four different initial classification maps acquired from different classifiers or image features. Second, an adaptive region around each pixel of the gray image is generated with two predefined parameters, and each adaptive region is applied to refine the corresponding pixel of each initial classified map. Finally, all the refined classified maps are merged to obtain the final classification map by coupling adaptive region and majority voting rules. In our experiments, three optical images with VHSR are used to evaluate the proposed ARPF. Compared with three typical relevant post-classification methods, the proposed ARPF can provide a classification map with less noise in visual performance and achieve higher quantitative accuracy while having an advantage in the constant detail of ground targets.

Original languageEnglish
Article number014524
JournalJournal of Applied Remote Sensing
Volume15
Issue number1
DOIs
Publication statusPublished - 31 Mar 2021

Bibliographical note

Funding Information:
The authors thank the editor-in-chief, associate editor, and reviewers for their insightful comments and suggestions. This work was supported by the foundation of Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area (No. 300102210524). The authors declare no conflicts of interest.

Publisher Copyright:
© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).

Other keywords

  • adaptive contextual information
  • land cover classification
  • very high-resolution optical image

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