BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING
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 chan...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2020-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/1629/2020/isprs-archives-XLIII-B3-2020-1629-2020.pdf |
| _version_ | 1811327749603721216 |
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| author | S. T. Seydi M. Hasanlou |
| author_facet | S. T. Seydi M. Hasanlou |
| author_sort | S. T. Seydi |
| collection | DOAJ |
| description | 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 <i>no-change</i> areas using image differencing algorithm (ID), (2) decision phase that it decides for detecting <i>change</i> 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%. |
| first_indexed | 2024-04-13T15:13:35Z |
| format | Article |
| id | doaj.art-97e1bdf885ca4109a58f4e0a4f04e88e |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-13T15:13:35Z |
| publishDate | 2020-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-97e1bdf885ca4109a58f4e0a4f04e88e2022-12-22T02:41:56ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342020-08-01XLIII-B3-20201629163310.5194/isprs-archives-XLIII-B3-2020-1629-2020BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNINGS. T. Seydi0M. Hasanlou1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, IranTimely 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 <i>no-change</i> areas using image differencing algorithm (ID), (2) decision phase that it decides for detecting <i>change</i> 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%.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/1629/2020/isprs-archives-XLIII-B3-2020-1629-2020.pdf |
| spellingShingle | S. T. Seydi M. Hasanlou BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING |
| title_full | BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING |
| title_fullStr | BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING |
| title_full_unstemmed | BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING |
| title_short | BINARY HYPERSPECTRAL CHANGE DETECTION BASED ON 3D CONVOLUTION DEEP LEARNING |
| title_sort | binary hyperspectral change detection based on 3d convolution deep learning |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2020/1629/2020/isprs-archives-XLIII-B3-2020-1629-2020.pdf |
| work_keys_str_mv | AT stseydi binaryhyperspectralchangedetectionbasedon3dconvolutiondeeplearning AT mhasanlou binaryhyperspectralchangedetectionbasedon3dconvolutiondeeplearning |