Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images
Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining par...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2017-10-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/9/11/1112 |
| _version_ | 1798031232152371200 |
|---|---|
| author | ZhiYong Lv WenZhong Shi XiaoCheng Zhou Jón Atli Benediktsson |
| author_facet | ZhiYong Lv WenZhong Shi XiaoCheng Zhou Jón Atli Benediktsson |
| author_sort | ZhiYong Lv |
| collection | DOAJ |
| description | Change detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution. |
| first_indexed | 2024-04-11T19:54:07Z |
| format | Article |
| id | doaj.art-cc209ce7b8414b39a6729baf83a48931 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:54:07Z |
| publishDate | 2017-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cc209ce7b8414b39a6729baf83a489312022-12-22T04:06:14ZengMDPI AGRemote Sensing2072-42922017-10-01911111210.3390/rs9111112rs9111112Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing ImagesZhiYong Lv0WenZhong Shi1XiaoCheng Zhou2Jón Atli Benediktsson3School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, ChinaKey Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350116, ChinaFaculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, IcelandChange detection is an increasingly important research topic in remote sensing application. Previous studies achieved land cover change detection (LCCD) using bi-temporal remote sensing images. However, many widely used methods detected change depending on a series of parameters, and determining parameters is time-consuming. Furthermore, numerous methods are data-dependent. Therefore, their degree of automation should be improved significantly. Three techniques, which consist of a semi-automatic change detection system, are proposed for LCCD to overcome the abovementioned drawbacks. The three techniques are as follows: (1) change magnitude image (CMI) noise reduction is based on Gaussian filter (GF), which is coupled with OTSU for reducing CMI noise automatically using an iterative optimization strategy; (2) a method based on histogram curve fitting is suggested to predict the threshold range for parameter determination; and (3) a modified region growing algorithm is built for iteratively constructing the final change detection map. The detection accuracies of the proposed system are investigated through four experiments with different bi-temporal image scenes. Compared with several widely used change detection methods, the proposed system can be applied to detect land cover change with high accuracy and flexibility. This work is an attempt to provide a change detection system that is compatible with remote sensing images with high and median-low spatial resolution.https://www.mdpi.com/2072-4292/9/11/1112semi-automatic change detection systemland cover change detectionremote sensing images |
| spellingShingle | ZhiYong Lv WenZhong Shi XiaoCheng Zhou Jón Atli Benediktsson Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images Remote Sensing semi-automatic change detection system land cover change detection remote sensing images |
| title | Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images |
| title_full | Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images |
| title_fullStr | Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images |
| title_full_unstemmed | Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images |
| title_short | Semi-Automatic System for Land Cover Change Detection Using Bi-Temporal Remote Sensing Images |
| title_sort | semi automatic system for land cover change detection using bi temporal remote sensing images |
| topic | semi-automatic change detection system land cover change detection remote sensing images |
| url | https://www.mdpi.com/2072-4292/9/11/1112 |
| work_keys_str_mv | AT zhiyonglv semiautomaticsystemforlandcoverchangedetectionusingbitemporalremotesensingimages AT wenzhongshi semiautomaticsystemforlandcoverchangedetectionusingbitemporalremotesensingimages AT xiaochengzhou semiautomaticsystemforlandcoverchangedetectionusingbitemporalremotesensingimages AT jonatlibenediktsson semiautomaticsystemforlandcoverchangedetectionusingbitemporalremotesensingimages |