Performances of frequency-based contextual classifier for land use/land cover using Cropcam UAV data

Traditional aerial images provided by satellite, manned aircraft or stock photography are often expensive, difficult to obtain or outdated. The Cropcam provides GPS based digital images on demand and real time data with high temporal resolution throughout the equatorial region where the sky is often covered by clouds. The images obtained by the Cropcam will allow producers to detect, locate, and have a better assessment of the actions required to overcome the problem of unclear images obtained by the satellite and manned aircraft in this area. A Pentax digital camera, model Optio A40, was used to capture images from the height of 320 meters on board the Cropcam UAV autopilot. The objective of this study is to evaluate the land use /land cover (LULC) features over Penang Island using the images obtained during the platform flying mission. The study also tests the effectiveness of Frequency-Based contextual classifier instead of conventional methods in a classification process in order to overcome or minimize the difficulty in classification of the mixed pixel area using high resolution images with spatial ground 8 cm. The technique was applied to the digital camera spectral bands (red, green and blue) to extract thematic information from the acquired scene by using PCI Geomatica 10.3 image processing software. Training sites were selected within each scene, and four LULC classes were assigned to each classifier. The accuracy assessment of each classification map produced was validated using the reference data sets consisting of a large number of samples collected per category. The results showed that the Frequency-Based contextual classifier produced superior results and achieved a high degree of accuracy. The study revealed that the Frequency-Based contextual classifier is effective and could be used for LULC classification using high resolution images of a small area of coverage acquired by the CropCam UAV.