Urban Classification by the Fusion of Thermal Infrared Hyperspectral and Visible Data

Abstract The 2014 Data Fusion Contest, organized by the Image Analysis and Data Fusion (IADF) Technical Committee of the IEEE Geoscience and Remote Sensing Societ y , involved two datasets acquired at different spectral ranges and spatial resolutions: a coarser-resolution long-wave infrared (LWIR, thermal infrared) hyperspectral data set and fine-resolution data acquired in the visible (VIS) wavelength range. In this article, a novel multi-level fusion approach is proposed to fully utilize the characteristics of these two different datasets to achieve improved urban land-use and land-cover classification. Specificall y , road extraction by fusing the classification result of the TI-HSI dataset and the segmentation result of the VIS dataset is first proposed. Thereafter, a novel gap inpainting method for the VIS data with the guidance of the TI-HSI data is presented to deal with the swath width inconsistency, and to facilitate an accurate spatial feature extraction step. The experimental results with the 2014 Data Fusion Contest datasets suggest that the proposed method can alleviate the multi-spectral-spatial resolution and multi-swath width problem to a great extent, and achieve an improved urban classification accuracy.

[1]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[2]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[3]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[4]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[5]  Dorin Comaniciu,et al.  Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[7]  G. Foody.  Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy , 2004 .

[8]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

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

[10]  G. Hay,et al.  Special Issue on Geographic Object-Based Image Analysis (GEOBIA). , 2010 .

[11]  Xiang Zhu,et al.  Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content , 2010, IEEE Transactions on Image Processing.

[12]  Rohit Maurya,et al.  Road extraction using K-Means clustering and morphological operations , 2011, 2011 International Conference on Image Information Processing.

[13]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[14]  Mario Chica-Olmo,et al.  Incorporating the downscaled landsat TM thermal band in land-cover classification using random forest , 2012 .

[15]  Xin Huang,et al.  A multilevel decision fusion approach for urban mapping using very high-resolution multi/hyperspectral imagery , 2012 .

[16]  Liangpei Zhang,et al.  A super-resolution reconstruction algorithm for hyperspectral images , 2012, Signal Process..

[17]  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.

[18]  Liangpei Zhang,et al.  Hyperspectral Image Restoration Using Low-Rank Matrix Recovery , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Liangpei Zhang,et al.  Building Change Detection From Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Hongyan Zhang,et al.  Column-generation kernel nonlocal joint collaborative representation for hyperspectral image classification , 2014 .

[21]  Lin Yan,et al.  Spectral-Angle-based Laplacian Eigenmaps for Nonlinear Dimensionality Reduction of Hyperspectral Imagery , 2014 .

[22]  Liangpei Zhang,et al.  An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Liangpei Zhang,et al.  Joint Collaborative Representation With Multitask Learning for Hyperspectral Image Classification , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Liangpei Zhang,et al.  Supervised Segmentation of Very High Resolution Images by the Use of Extended Morphological Attribute Profiles and a Sparse Transform , 2014, IEEE Geoscience and Remote Sensing Letters.

[25]  G. Miliaresis,et al.  Daily Temperature Oscillation Enhancement of Multitemporal LST Imagery , 2014 .

[26]  M. S. Moran,et al.  Mapping Impervious Surfaces Using Object-oriented Classification in a Semiarid Urban Region , 2014 .

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