Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 425-430, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-425-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 425-430, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-425-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Apr 2018

30 Apr 2018

PURIFICATION OF TRAINING SAMPLES BASED ON SPECTRAL FEATURE AND SUPERPIXEL SEGMENTATION

X. Guan1, W. Qi1,2, J. He1, Q. Wen1, T. Chen1, and Z. Wang3 X. Guan et al.
  • 1Twenty-First Century Aerospace Technology Co., Ltd., 100096, Beijing, China
  • 2Beijing Twenty-First Century Science and Technology Development Co., Ltd., 100096, Beijing, China
  • 3Beijing Engineering Research Centre of Small Satellite Remote Sensing Information, Beijing, 10096, China

Keywords: Beijing-2 Satellite imagery, Training Samples, Purification, Spectral Feature, Superpixel Segmentation

Abstract. Remote sensing image classification is an effective way to extract information from large volumes of high-spatial resolution remote sensing images. Generally, supervised image classification relies on abundant and high-precision training data, which is often manually interpreted by human experts to provide ground truth for training and evaluating the performance of the classifier. Remote sensing enterprises accumulated lots of manually interpreted products from early lower-spatial resolution remote sensing images by executing their routine research and business programs. However, these manually interpreted products may not match the very high resolution (VHR) image properly because of different dates or spatial resolution of both data, thus, hindering suitability of manually interpreted products in training classification models, or small coverage area of these manually interpreted products. We also face similar problems in our laboratory in 21st Century Aerospace Technology Co. Ltd (short for 21AT). In this work, we propose a method to purify the interpreted product to match newly available VHRI data and provide the best training data for supervised image classifiers in VHR image classification. And results indicate that our proposed method can efficiently purify the input data for future machine learning use.