Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. ,  investigate their applicability for land cover change detection in forest regions. Firstly, multidate remote sensing images are co-registered and  have stacked the NDVI index layers of two dates in red, green, blue bands composite to perform a supervised classification. Secondly, sample pixels re manually selected from changed and unchanged area to be used in the training stage. Thirdly, for each pixel SVM produces a single output through its decision function, high detection overall accuracy (>96%) and overall Kappa coefficient (>0.89) re achieved using two landsat images covering an 8-years period in study area. Lastly, SVM-based change detection with different kernel functions was compared using statistical evaluations.
['Dengkui Mo', 'Hui Lin', 'Jiping Li', 'Hua Sun', 'Zhuo Zhang', 'Yujiu Xiong']
I am writing a research paper on a idea A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area
[{"from": "human", "value": "I am writing a research paper on a idea A SVM-Based Change Detection Method from Bi-Temporal Remote Sensing Images in Forest Area"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The reliability of support vector machines for classifying multi-spectral images of remote sensing has been proven in various studies. ,  investigate their applicability for land cover change detection in forest regions. Firstly, multidate remote sensing images are co-registered and  have stacked the NDVI index layers of two dates in red, green, blue bands composite to perform a supervised classification. Secondly, sample pixels re manually selected from changed and unchanged area to be used in the training stage. Thirdly, for each pixel SVM produces a single output through its decision function, high detection overall accuracy (>96%) and overall Kappa coefficient (>0.89) re achieved using two landsat images covering an 8-years period in study area. Lastly, SVM-based change detection with different kernel functions was compared using statistical evaluations."}]