Foreword to the Special Issue on Hyperspectral Remote Sensing and Imaging Spectroscopy

The twenty six papers in this special issue focus on the technologies of hyperspectral remote sensing (HRS)and imaging spectroscopy. HRS has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a diverse range of platforms, including terrestrial in-situ imaging platforms, unmanned and manned aerial vehicles, and satellite platforms. By virtue of imaging over a wide range of spectral wavelengths, it is possible to characterize object specific properties very accurately. As a result, hyperspectral imaging (also known as imaging spectroscopy) has gained popularity for a wide variety of applications, including environment monitoring, precision agriculture, mineralogy, forestry, urban planning, and defense applications. The increased analysis capability comes at a cost—there are a variety of challenges that must be overcome for robust image analysis of such data, including high dimensionality, limited sample size for training supervised models, noise and atmospheric affects, mixed pixels, etc. The papers in this issue represent some of the recent developments in image analysis algorithms and unique applications of hyperspectral imaging data.

[1]  Jie Geng,et al.  Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[2]  Shuang Wang,et al.  Class-Level Joint Sparse Representation for Multifeature-Based Hyperspectral Image Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Shuyuan Yang,et al.  Fuzzy Signature-Based Discriminative Subspace Projection for Hyperspectral Data Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Jon Atli Benediktsson,et al.  Spatial–Spectral Hyperspectral Image Classification Using Random Multiscale Representation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Davoud Ashourloo,et al.  An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Rupert Müller,et al.  A New Approach for Endmember Extraction and Clustering Addressing Inter- and Intra-Class Variability via Multiscaled-Band Partitioning , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[7]  Guangliang Chen,et al.  High-Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  Wen Hu,et al.  Adaptive Sampling by Dictionary Learning for Hyperspectral Imaging , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Thomas Koellner,et al.  Mapping Fractional Land Use and Land Cover in a Monsoon Region: The Effects of Data Processing Options , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Xiuping Jia,et al.  Hybrid Norm Pursuit Method for Hyperspectral Image Reconstruction , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Birgit Kleinschmit,et al.  Utilizing a PLSR-Based Band-Selection Procedure for Spectral Feature Characterization of Floristic Gradients , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Weiwei Sun,et al.  A Dissimilarity-Weighted Sparse Self-Representation Method for Band Selection in Hyperspectral Imagery Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Christoph Emmerling,et al.  Quantification of Soil Variables in a Heterogeneous Soil Region With VIS–NIR–SWIR Data Using Different Statistical Sampling and Modeling Strategies , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Xin Wu,et al.  GPU-Based Parallel Design of the Hyperspectral Signal Subspace Identification by Minimum Error (HySime) , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Hermann Kaufmann,et al.  Combining Field and Imaging Spectroscopy to Map Soil Organic Carbon in a Semiarid Environment , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Miguel Velez-Reyes,et al.  A Spectrally Weighted Structure Tensor for Hyperspectral Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Qian Du,et al.  Low-Rank Subspace Representation for Supervised and Unsupervised Classification of Hyperspectral Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Jing-Hao Xue,et al.  Denoising of Hyperspectral Images Using Group Low-Rank Representation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Liang Xiao,et al.  Bilayer Elastic Net Regression Model for Supervised Spectral-Spatial Hyperspectral Image Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Fang Liu,et al.  Adaptive Nonlocal Spatial–Spectral Kernel for Hyperspectral Imagery Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Liangyun Liu,et al.  Correcting Bidirectional Effect for Multiple-Flightline Aerial Images Using a Semiempirical Kernel-Based Model , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Yukio Kosugi,et al.  Development of a Low-Cost Hyperspectral Whiskbroom Imager Using an Optical Fiber Bundle, a Swing Mirror, and Compact Spectrometers , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  Antonio J. Plaza,et al.  Foreword to the Special Issue on Hyperspectral Image and Signal Processing , 2012, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

[24]  Saurabh Prasad,et al.  Spectral-Angle-Based Discriminant Analysis of Hyperspectral Data for Robustness to Varying Illumination , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Shuyuan Yang,et al.  Spectral–Spatial KerSparseBands Selector , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Jorge E. Pezoa,et al.  Multidimensional Striping Noise Compensation in Hyperspectral Imaging: Exploiting Hypercubes’ Spatial, Spectral, and Temporal Redundancy , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Angshul Majumdar,et al.  Hyperspectral Unmixing in the Presence of Mixed Noise Using Joint-Sparsity and Total Variation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[28]  Xia Zhang,et al.  Crop Classification Based on Feature Band Set Construction and Object-Oriented Approach Using Hyperspectral Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Peijun Du,et al.  Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications , 2013 .

[30]  Qian Du,et al.  Hyperspectral Image Classification by Fusing Collaborative and Sparse Representations , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  Antonio J. Plaza,et al.  A New Genetic Method for Subpixel Mapping Using Hyperspectral Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[32]  Ying Zhao,et al.  Evaluation of the Quasi-Analytical Algorithm (QAA) for Estimating Total Absorption Coefficient of Turbid Inland Waters in Northeast China , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[33]  Licheng Jiao,et al.  Automatic Band Selection Using Spatial-Structure Information and Classifier-Based Clustering , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  Matthew J. Hoffman,et al.  Integrating Hyperspectral Likelihoods in a Multidimensional Assignment Algorithm for Aerial Vehicle Tracking , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[35]  Liangpei Zhang,et al.  Sparsity-Regularized Robust Non-Negative Matrix Factorization for Hyperspectral Unmixing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[36]  Qiang Song,et al.  Spectral–Spatial Feature Learning Using Cluster-Based Group Sparse Coding for Hyperspectral Image Classification , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Chein-I Chang,et al.  Comparative Study and Analysis Among ATGP, VCA, and SGA for Finding Endmembers in Hyperspectral Imagery , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[38]  Li Liu,et al.  Evaluation of Temperature and Emissivity Retrieval using Spectral Smoothness Method for Low-Emissivity Materials , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[39]  Onisimo Mutanga,et al.  Discriminating Rangeland Management Practices Using Simulated HyspIRI, Landsat 8 OLI, Sentinel 2 MSI, and VENµS Spectral Data , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  Antonio J. Plaza,et al.  Harmonic Mixture Modeling for Efficient Nonlinear Hyperspectral Unmixing , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  Wilfried Philips,et al.  Feature Extraction of Hyperspectral Images With Semisupervised Graph Learning , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  Youshen Xia,et al.  Poissonian Hyperspectral Image Superresolution Using Alternating Direction Optimization , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  Antonio J. Plaza,et al.  FPGA Implementation of an Algorithm for Automatically Detecting Targets in Remotely Sensed Hyperspectral Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.