Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient

special section guest editorial airborne hyperspectral remote sensing of urban environments

University of Pavia, Department of Electrical, Computer and Biomedical Engineering, ItalyRemote sensing is a very useful tool in retrieving urban information in a timely, detailed, andcost-effective manner to assist various planning and management activities. Hyperspectralremote sensing has been of great interest to the scientific community since its emergence inthe 1980s, due to its very high spectral resolution providing the potential of finer material detec-tion, classification, identification, and quantification, compared to the traditional multispectralremote sensing. With the advance of computing facilities and more airborne high-spatial-reso-lution hyperspectral image data becoming available, many investigations on its real applicationsare taking place. In particular, urban environments are characterized by heterogeneous surfacecovers with significant spatial and spectral variations, and airborne hyperspectral imagery withhigh spatial and spectral resolutions offers an effective tool to analyze complex urban scenes.The objectiveof this special section of the Journal of Applied Remote Sensing is to provide asnapshot of status, potentials, and challenges of high-spatial-resolution hyperspectral imagery inurban feature extraction and land use interpretation in support of urban monitoring and man-agement decisions. This section includes twelve papers that cover four major topics: urban landuse and land cover classification, impervious surface mapping, built-up land analysis, and urbansurface water mapping.There are nine papers about urban land use and land cover classification. "Hyperspectralimage classification with improved local-region filters" by Ran et al. proposes two local-regionfilters, i.e., spatial adaptive weighted filter and collaborative-representation-based filter, for spa-tial feature extraction, thereby improving classification of urban hyperspectral imagery. "Edge-constrained Markov random field classification by integrating hyperspectral image with LiDARdata over urban areas" by Ni et al. adopts an edge-constrained Markov random field method foraccurate land cover classification over urban areas with hyperspectral image and LiDAR data."Combining data mining algorithm and object-based image analysis for detailed urban mappingof hyperspectral images" by Hamedianfar et al. explores the combined performance of a datamining algorithm and object-based image analysis, which can produce high accuracy of urbansurfacemapping."Dynamicclassifierselectionusingspectral-spatial information forhyperspec-tralimageclassification"bySuetal.proposestheintegrationofspectralfeatureswithvolumetrictextural features to improve the classification performance for urban hyperspectral images."Representation-based classifications with Markov random field model for hyperspectralurban data" by Xiong et al. improves representation-based classification by considering spa-tial-contextualinformationderivedfromaMarkovrandomfield."Classificationofhyperspectralurban data using adaptivesimultaneous orthogonal matching pursuit" by Zou et al. improves theclassification performance of a joint sparsity model, i.e., simultaneous orthogonal matching pur-suit, by using a priori segmentation map.Othertechniques,suchaslinearunmixinganddimensionalityreduction,arealsoinvestigatedin conjunction with urban surface mapping.Among the nine papersonclassification,twopapersconsider linear unmixing, which are "Unsupervised classification strategy utilizing an endmem-ber extraction technique for airborne hyperspectral remotely sensed imagery" by Xu et al., and"Endmembernumberestimationforhyperspectralimagerybasedonvertexcomponentanalysis"by Liu et al. One paper studies the impact of dimensionality reduction (through band selection)on classification accuracy, which is "Ant colony optimization-based supervised and unsuper-vised band selections for hyperspectral urban data classification" by Gao et al

ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

A new kernel method for hyperspectral image feature extraction

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required

GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection

Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts

Dimensionality Reduction and Classification feature using Mutual Information applied to Hyperspectral Images : A Filter strategy based algorithm

Hyperspectral images (HIS) classification is a high technical remote sensing tool. The goal is to reproduce a thematic map that will be compared with a reference ground truth map (GT), constructed by expecting the region. The HIS contains more than a hundred bidirectional measures, called bands (or simply images), of the same region. They are taken at juxtaposed frequencies. Unfortunately, some bands contain redundant information, others are affected by the noise, and the high dimensionality of features made the accuracy of classification lower. The problematic is how to find the good bands to classify the pixels of regions. Some methods use Mutual Information (MI) and threshold, to select relevant bands, without treatment of redundancy. Others control and eliminate redundancy by selecting the band top ranking the MI, and if its neighbors have sensibly the same MI with the GT, they will be considered redundant and so discarded. This is the most inconvenient of this method, because this avoids the advantage of hyperspectral images: some precious information can be discarded. In this paper we'll accept the useful redundancy. A band contains useful redundancy if it contributes to produce an estimated reference map that has higher MI with the GT.nTo control redundancy, we introduce a complementary threshold added to last value of MI. This process is a Filter strategy; it gets a better performance of classification accuracy and not expensive, but less preferment than Wrapper strategy.Comment: 11 pages, 5 figures, journal pape