High-Resolution Remote Sensing Image Classification Using Associative Hierarchical CRF Considering Segmentation Quality

Yun Yang (Corresponding Author), A. Stein, V.A. Tolpekin, Yang Zhang

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
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Abstract

This letter proposes an associative hierarchical conditional random field (AHCRF) model to improve the classification accuracy of high-resolution remote sensing images. It considers segmentation quality of superpixels, avoids a time-consuming selection of optimal scale parameters, and alleviates the problem of classification accuracy sensitive to undersegmentation errors that is present in traditional object-oriented classification methods. The model is built on a graph hierarchy, including the pixel layer as a base layer and multiple superpixel layers derived from a mean shift presegmentation. It extracts clustered features of pixels for superpixels at each layer and then defines the potentials of the AHCRF model. We suggest a weighted version of the interlayer potential using the size of a superpixel as a measure to reflect segmentation quality. In this way, erroneously labeled pixels of a superpixel are penalized. Experiments are presented using a part of the downsampled Vaihingen data from the ISPRS benchmark data set. Results confirm that our model shows more than 80% overall classification accuracy and is superior to the original AHCRF model and comparable to other models. It also alleviates the choosing of suitable segmentation parameters.

Original languageEnglish
Pages (from-to)754-758
Number of pages5
JournalIEEE geoscience and remote sensing letters
Volume15
Issue number5
Early online date14 Mar 2018
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • ITC-ISI-JOURNAL-ARTICLE

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