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Deep learning model improvement for building change detection by including local remote sensing training datasets | ||
| Earth Observation and Geomatics Engineering | ||
| دوره 7، شماره 1، شهریور 2023 اصل مقاله (1.25 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2024.372323.1146 | ||
| نویسندگان | ||
| Saeid Abdolian1؛ Ali Esmaeily1؛ Mohammad Reza Saradjian Maralan* 2 | ||
| 1Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Mahan, Kerman, Iran | ||
| 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran | ||
| چکیده | ||
| ABSTRACT Deep learning networks which have been trained using known datasets normally do not produce convincing results when used on other datasets. In this study, it has been shown that the training of previously developed networks with the newly included local training datasets greatly increases the accuracy. Accordingly, the aim of this study is to improve a DL model by including local data along with existing known dataset for building change detection. The STANet has not been previously used in industrial areas where there are different building characteristics. High spatial resolution satellite images have been used for buildings change detection in this study. The STANet has been implemented using a new dataset comprising from an existing known dataset together with two local building datasets. The training and testing of the STANet is investigated in incremental training data in three stages. In the first stage, STANet network that was trained by already existing LEVIR-CD dataset was tested, but it did not produce reasonable result. In the second stage, the network which was trained by combining LEVIR-CD and a local dataset was tested and obtained a better result. In the third stage, the network which was trained by combining LEVIR-CD dataset, local dataset from stage2 and another more specific and complementary local dataset, was tested and reached a much better result than the previous two stages. As a result, for the trained network with the corresponding data, high average, recall, accuracy and precision achieved. In conclusion, the significance of using local corresponding training dataset on a DL model has been exhibited. | ||
| کلیدواژه‌ها | ||
| Deep learning improvement؛ Local training datasets؛ Change detection؛ Newly constructed buildings | ||
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آمار تعداد مشاهده مقاله: 134 تعداد دریافت فایل اصل مقاله: 204 |
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