Improvement in the Detection of Land Cover Classes Using the Worldview-2 Imagery

Recent advances in satellite and airborne sensors make spatial and multispectral high-resolution imagery effortlessly available. These advances give the chance to address and solve some old problems related to the poor spatial or spectral resolution; such as the lack of details for certain features or the inability of the traditional classifiers to detect some land cover types due to the missing of important parts of the spectrum. High-resolution imagery is particularly well suited to urban applications. Previous High-resolution imagery data sources (such as IKONOS or QuickBird) did show the required spatial details necessary to provide a good urban planning solution, but they fail to represent a reasonable spectral resolution, as they only contain three visible bands and one near infra red band, which hinders the ability of traditional spectral classifiers to accurately detect some land-cover types . The increased spatial information in sub-meter imagery accompanied with a low spectral resolution make the process of image classification using traditional supervised or unsupervised spectral classification algorithms a very difficult task. The previous limitations led many researchers to the object-oriented solution to provide a reliable classification results. This study evaluated how spectral and spatial resolution of the new satellite World View-2 influences the quality of the classification using only traditional spectral classifiers which mainly depends on the information provided by the 8-spectral bands ranging from the visible to mid-infra red bands, investigating the rule of the new bands in accurately distinguishing detailed land cover types. This study assesses the accuracy of the classification for a data set of WorldView-2 satellite imagery when using the full 8-bands (data set I), and when using only the three-visible bands and the NIR band only (data set II). The study uses the Maximum Likelihood Classifier method for extracting land cover information. Also the study introduces new band ratios in a multi-layer classification tree analysis to extract certain features such as shadows and asphalt roads.