The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1629–1633, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1629-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1629–1633, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1629-2020

  22 Aug 2020

22 Aug 2020

BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING

S. T. Seydi and M. Hasanlou S. T. Seydi and M. Hasanlou
  • School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran

Keywords: hyperspectral change detection, deep learning, 3D-convolution, image differencing

Abstract. Timely and accurate change detection of Earth’s surface features is extremely important for understanding relationships and interactions between human and natural phenomena to promote better decision making. The bi-temporal hyperspectral imagery has a high potential for the detection of surface changes. However, the extraction of changes from bi-temporal hyperspectral imagery due to special content of data, and environment conditions (atmospheric condition), change into challenging task. To this end, this research proposed a change detection framework based on deep learning using bi-temporal hyperspectral imagery. The proposed framework is applied in two main steps: (1) predict phase that the change areas highlighted from no-change areas using image differencing algorithm (ID), (2) decision phase that it decides for detecting change pixels based on 3D convolution neural network (CNN). The efficiency of the presented method is evaluated using Hyperion multi-temporal hyperspectral imagery. To evaluate the performance of the proposed method, two bi-temporal hyperspectral Hyperion with a variety of land cover classes were used. The results show that the proposed method has high accuracy and low false alarms rate: overall accuracy is more than 95%, and the kappa coefficient is greater than 0.9 and the miss-detection is lower than 10% and the false rate is lower than 4%.