Abstract
In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from four to 35 different classes) from a Mediterranean bioregion in Iberian, Spain. We used various MISR band groups from nadir to blue, green, red, and NIR channels at nadir and off-nadir. The MISR data utilized here were provided by the L1B2T product (275 m spatial resolution) and belonged to two different orbits. We performed 23 classifications with the k-means algorithm to test multiangular data, number of clusters, and iteration effects. Our findings confirmed that the multiangular information, in addition to the multispectral information used as the input of the k-means algorithm, improves the land cover classification accuracy, and this improvement increased with the level of disaggregation. A very large number of clusters produced even better improvements than multiangular data.
Highlights
Remote sensing techniques offer a unique environmental monitoring capability that covers large geographical areas in a cost-efficient manner
Traditional remote sensing classification depends on distinctive signatures for the land cover categories or types in the band set that is being used
The study area in this research was the intersection of Multiangle Imaging Spectroradiometer (MISR) path 201, and mainland Spain
Summary
Remote sensing techniques offer a unique environmental monitoring capability that covers large geographical areas in a cost-efficient manner. This science can capture important information about the Earths land, atmosphere, and body of waters. One of the applications of remote sensing is to classify images to obtain classified maps. Classification in remote sensing groups the pixels of an image into a set of classes, such that pixels in the same category have similar properties. Most of the image classifications are based on the detection of the spectral response patterns of land cover classes [20]. Traditional remote sensing classification depends on distinctive signatures for the land cover categories or types in the band set that is being used. The application of remote sensing data for land cover and land use mapping, and their changes, is key to many diverse applications that have been mentioned before [22,23]
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