Investigating the effect of radar images in classifying land use classes in machine learning based algorithms
Article Type:
Research/Original Article (بدون رتبه معتبر)

Acquiring knowledge about the types of land uses and the stages of their change provides basic and very important information to researchers and decision makers. One of the most common and useful methods in remote sensing is to access the maximum information contained in satellite data by combining radar and optical satellite images. In general, the main purpose of this study was to investigate the effect of the presence of SAR images in the classification of optical multi-temporal satellite images in machine learning-based classification algorithms, including random forest, Cart Decision Tree and Support Vector Machine. In the above paper, the Normalized Difference Vegetation Index (NDVI) dataset, along with slope layers, a digital elevation model and a corrected Sentinel-2 satellite image was supervised by the three methods mentioned. Once again, this was done with the presence of the Sentinel-1 satellite SAR image database. Finally, in the post-processing stage, the individual pixels were connected to neighboring classes. This was done by majority filtering. The final results were validated with ground data. The results showed that in the study of all classes, the overall accuracy and kappa coefficient in the presence of SAR dataset and for all three classification methods improved by only 3%, but in the one-to-one study of the classes, the producer accuracy of the random forest method in the dual agriculture class improved. It has been significant and its value has increased from 0.74 to 0.84. In the support vector machine method, dry farming and orchard classes have had a more significant improvement, which have increased from 0.75 and 0.78 to 0.84 and 0.92, respectively. Finally, it can be said that the addition of radar images to the classification has a positive and significant effect only in the mentioned classes, and also the obvious advantage of the random forest method compared to other methods is quite obvious.

Journal of Land Ecology, Volume:2 Issue: 2, 2023
137 to 150  
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 990,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe for 50 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!