Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discusses landcover classification using high density airborne LiDAR data and multispectral imagery. The study area is located at the Duursche Waarden floodplain, the Netherlands. The density of the FLI-MAP 400 LiDAR system is beten 50 and 100 points per m 2 . Other than height and intensity, the LiDAR system also measures spectral information (Red, Green, and Blue). Several features are created for height, intensity, Red, Green, and Blue. The landcover classification process is divided into Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers. Each classifier is used on three different datasets: 1) FLI-MAP 400-generated multispectral images, 2) LiDAR-derived features, and 3) a combination of the multispectral images and the LiDAR-derived features. The results show that the SVM method produces better classification results than the ML method. Landcover classification based on the combination of LiDAR-derived features and multispectral images produces better results than classification based on either dataset only.
['Muhammad Zulkarnain Abdul Rahman', 'Wan Hazli Wan Kadir', 'Abd Wahid Rasib', 'Azman Ariffin', 'Khamarrul Azahari Razak']
I am writing a research paper on a idea Integration of high density airborne LiDAR and high spatial resolution image for landcover classification
[{"from": "human", "value": "I am writing a research paper on a idea Integration of high density airborne LiDAR and high spatial resolution image for landcover classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  discusses landcover classification using high density airborne LiDAR data and multispectral imagery. The study area is located at the Duursche Waarden floodplain, the Netherlands. The density of the FLI-MAP 400 LiDAR system is beten 50 and 100 points per m 2 . Other than height and intensity, the LiDAR system also measures spectral information (Red, Green, and Blue). Several features are created for height, intensity, Red, Green, and Blue. The landcover classification process is divided into Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers. Each classifier is used on three different datasets: 1) FLI-MAP 400-generated multispectral images, 2) LiDAR-derived features, and 3) a combination of the multispectral images and the LiDAR-derived features. The results show that the SVM method produces better classification results than the ML method. Landcover classification based on the combination of LiDAR-derived features and multispectral images produces better results than classification based on either dataset only."}]