Generalized Differential Morphological Profiles for Remote Sensing Image Classification

Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF “out-of-bag” error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional WorldView-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information.

[1]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[2]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[3]  Jon Atli Benediktsson,et al.  Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis , 2011, IEEE Geoscience and Remote Sensing Letters.

[4]  Liangpei Zhang,et al.  An Adaptive Multiscale Information Fusion Approach for Feature Extraction and Classification of IKONOS Multispectral Imagery Over Urban Areas , 2007, IEEE Geoscience and Remote Sensing Letters.

[5]  Aleksandra Pizurica,et al.  Classification of Hyperspectral Data Over Urban Areas Using Directional Morphological Profiles and Semi-Supervised Feature Extraction , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Tom Bylander,et al.  Estimating Generalization Error on Two-Class Datasets Using Out-of-Bag Estimates , 2002, Machine Learning.

[7]  Jon Atli Benediktsson,et al.  Multiple Morphological Profiles From Multicomponent-Base Images for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[9]  Jon Atli Benediktsson,et al.  Classification and feature extraction for remote sensing images from urban areas based on morphological transformations , 2003, IEEE Trans. Geosci. Remote. Sens..

[10]  Qian Du,et al.  Gabor-Filtering-Based Nearest Regularized Subspace for Hyperspectral Image Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Jonathan Cheung-Wai Chan,et al.  Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Johannes R. Sveinsson,et al.  Classification of hyperspectral data from urban areas based on extended morphological profiles , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Jon Atli Benediktsson,et al.  Automatic Framework for Spectral–Spatial Classification Based on Supervised Feature Extraction and Morphological Attribute Profiles , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Jon Atli Benediktsson,et al.  Classification of remote sensing images from urban areas using a fuzzy possibilistic model , 2006, IEEE Geoscience and Remote Sensing Letters.

[15]  Jon Atli Benediktsson,et al.  Morphological Attribute Profiles for the Analysis of Very High Resolution Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Liangpei Zhang,et al.  An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[17]  N. Lam,et al.  Wavelets for Urban Spatial Feature Discrimination: Comparisons with Fractal, Spatial Autocorrelation, and Spatial Co-Occurrence Approaches , 2004 .

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[19]  Jean Serra,et al.  Image Analysis and Mathematical Morphology , 1983 .

[20]  Jon Atli Benediktsson,et al.  A Survey on Spectral–Spatial Classification Techniques Based on Attribute Profiles , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Martino Pesaresi,et al.  A Robust Built-Up Area Presence Index by Anisotropic Rotation-Invariant Textural Measure , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Jon Atli Benediktsson,et al.  A new approach for the morphological segmentation of high-resolution satellite imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[23]  Chandrika Kamath,et al.  Retrieval using texture features in high-resolution multispectral satellite imagery , 2004, SPIE Defense + Commercial Sensing.

[24]  Mahesh Pal,et al.  Random forest classifier for remote sensing classification , 2005 .

[25]  Pierre Soille,et al.  Morphological Image Analysis: Principles and Applications , 2003 .

[26]  Jon Atli Benediktsson,et al.  Classification of hyperspectral data using extended attribute profiles based on supervised and unsupervised feature extraction techniques , 2012 .

[27]  Liangpei Zhang,et al.  Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery , 2009, IEEE Geoscience and Remote Sensing Letters.

[28]  Liangpei Zhang,et al.  A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation , 2014, Remote. Sens..

[29]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Chang-qing Zhu,et al.  Study of remote sensing image texture analysis and classification using wavelet , 1998 .