Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Abstract

Land-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen's kappa coefficient of 0.84 and an overall accuracy of 85%.

Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns

“…With the availability of pan-sharpened VHR satellite imagery, classification of small scale manmade structures in urban environments have become of great interest. In the last decade, Object-Based Image Analysis (OBIA) has proved to be an effective approach to deal with this problem [Carleer and Wolff, 2006;Blaschke, 2010;Lu et al, 2010;Myint et al, 2011;Weng, 2012;Gianinetto et al, 2014]. OBIA does not use individual pixels but pixel groups representing meaningful segments (or objects), which have been segmented according to different criteria before the classification stage is carried out.…”

“…Perhaps the most popular and widely used approach to extract image textural information are the second order texture features based on the so-called graylevel co-occurrence matrix (GLCM) proposed by Haralick et al [1973]. The inclusion of texture features seems to significantly improve classification accuracy of satellite images [Puissant et al, 2005;Carleer and Wolff, 2006;Agüera et al, 2008;Murray et al, 2010;Ozdemira and Karnieli, 2011;Stumpf and Kerle, 2011;Eckert 2012;Longbotham et al, 2012;Aguilar et al, 2013, Gianinetto et al, 2014. Many feature extraction algorithms based on the GLCM have been proposed in the literature.…”

An Adaptive Morphological Mean Filter for Very High-Resolution Remote Sensing Image Processing

“…On the one hand, VHR image increases the correlation among pixels, and this results in a relevant amount of spatial features in VHR image, these features are useful for visual interpretation and classification (Wang, Dai et al 2012). On the other hand, since the constraints of remote sensing technique, images with a very high resolution are typically limited to generally 3~4 spectral bans(worldview-2 is 0.41meter and 8-bands) (Gianinetto, Rusmini et al 2014). In other words, high resolution and low spectral properties result in an increment of the intra-class variance and a decrease of the inter-class variance, which leads original spectral noise into the classifying thematic map and enhance the difficulty of separability (Carleer, Debeir et al 2004, Blaschke 2010.…”

A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

“…However, given the limitation of remote sensing technology, VHR images are often coupled with poor radiometry, with less than three or five spectral bands. Therefore, this characteristic (i.e., a high spatial resolution but poor spectral radiometry) does not necessarily reflect higher classification accuracies when the images are applied in practice [4][5][6].…”