Optimal Clustering Framework for Hyperspectral Band Selection

Band selection, by choosing a set of representative bands in hyperspectral image (HSI), is an effective method to reduce the redundant information without compromising the original contents. Recently, various unsupervised band selection methods have been proposed, but most of them are based on approximation algorithms which can only obtain suboptimal solutions toward a specific objective function. This paper focuses on clustering-based band selection, and proposes a new framework to solve the above dilemma, claiming the following contributions: 1) An optimal clustering framework (OCF), which can obtain the optimal clustering result for a particular form of objective function under a reasonable constraint. 2) A rank on clusters strategy (RCS), which provides an effective criterion to select bands on existing clustering structure. 3) An automatic method to determine the number of the required bands, which can better evaluate the distinctive information produced by certain number of bands. In experiments, the proposed algorithm is compared to some state-of-the-art competitors. According to the experimental results, the proposed algorithm is robust and significantly outperform the other methods on various data sets

Building Footprint Generation Using Improved Generative Adversarial Networks

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameters tuning.Comment: 5 page

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

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Comment: 14 pages, 7 figures, TGRS201

Band Ranking via Extended Coefficient of Variation for Hyperspectral Band Selection

Hundreds of narrow bands over a continuous spectral range make hyperspectral imagery rich in information about objects, while at the same time causing the neighboring bands to be highly correlated. Band selection is a technique that provides clear physical-meaning results for hyperspectral dimensional reduction, alleviating the difficulty for transferring and processing hyperspectral images caused by a property of hyperspectral images: large data volumes. In this study, a simple and efficient band ranking via extended coefficient of variation (BRECV) is proposed for unsupervised hyperspectral band selection. The naive idea of the BRECV algorithm is to select bands with relatively smaller means and lager standard deviations compared to their adjacent bands. To make this simple idea into an algorithm, and inspired by coefficient of variation (CV), we constructed an extended CV matrix for every three adjacent bands to study the changes of means and standard deviations, and accordingly propose a criterion to allocate values to each band for ranking. A derived unsupervised band selection based on the same idea while using entropy is also presented. Though the underlying idea is quite simple, and both cluster and optimization methods are not used, the BRECV method acquires qualitatively the same level of classification accuracy, compared with some state-of-the-art band selection methodsPeer reviewe

Locality Regularized Robust-PCRC: A Novel Simultaneous Feature Extraction and Classification Framework for Hyperspectral Images

Despite the successful applications of probabilistic collaborative representation classification (PCRC) in pattern classification, it still suffers from two challenges when being applied on hyperspectral images (HSIs) classification: 1) ineffective feature extraction in HSIs under noisy situation; and 2) lack of prior information for HSIs classification. To tackle the first problem existed in PCRC, we impose the sparse representation to PCRC, i.e., to replace the 2-norm with 1-norm for effective feature extraction under noisy condition. In order to utilize the prior information in HSIs, we first introduce the Euclidean distance (ED) between the training samples and the testing samples for the PCRC to improve the performance of PCRC. Then, we bring the coordinate information (CI) of the HSIs into the proposed model, which finally leads to the proposed locality regularized robust PCRC (LRR-PCRC). Experimental results show the proposed LRR-PCRC outperformed PCRC and other state-of-the-art pattern recognition and machine learning algorithms

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination). To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semi-supervised hyperspectral dimensionality reduction (HDR), called joint and progressive subspace analysis (JPSA). The JPSA learns a high-level, semantically meaningful, joint spatial-spectral feature representation from hyperspectral data by 1) jointly learning latent subspaces and a linear classifier to find an effective projection direction favorable for classification; 2) progressively searching several intermediate states of subspaces to approach an optimal mapping from the original space to a potential more discriminative subspace; 3) spatially and spectrally aligning manifold structure in each learned latent subspace in order to preserve the same or similar topological property between the compressed data and the original data. A simple but effective classifier, i.e., nearest neighbor (NN), is explored as a potential application for validating the algorithm performance of different HDR approaches. Extensive experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely-used hyperspectral datasets: Indian Pines (92.98\%) and the University of Houston (86.09\%) in comparison with previous state-of-the-art HDR methods. The demo of this basic work (i.e., ECCV2018) is openly available at https://github.com/danfenghong/ECCV2018_J-Play

Application-Dependent Wavelength Selection For Hyperspectral Imaging Technologies

Hyperspectral imaging has proven to provide benefits in numerous application domains, including agriculture, biomedicine, remote sensing, and food quality management. Unlike standard color imagery composed of these broad wavelength bands, hyperspectral images are collected over numerous (possibly hundreds) of narrow wavelength bands, thereby offering vastly more information content than standard imagery. It is this higher information content which enables improved performance in complex classification and regression tasks. However, this successful technology is not without its disadvantages which include high cost, slow data capture, high data storage requirements, and computational complexity. This research seeks to overcome these disadvantages through the development of algorithms and methods to enable the benefits of hyperspectral imaging in inexpensive portable devices that collect spectral data at only a handful (i.e., 5-7) of wavelengths specifically selected for the application of interest.This dissertation focuses on two applications of practical interest: fish fillet species classification for the prevention of food fraud and tissue oxygenation estimation for wound monitoring. Genetic algorithm, self-organizing map, and simulated annealing approaches for wavelength selection are investigated for the first application, combined with common machine learning classifiers for species classification. The simulated annealing approach for wavelength selection is carried over to the wound monitoring application and is combined with the Extended Modified Lambert-Beer law, a tissue oxygenation method that has proven to be robust to differences in melanin concentrations. Analyses for this second application included spectral convolutions to represent data collection with the envisioned inexpensive portable devices. Results of this research showed that high species classification accuracy (\u3e 90%) and low tissue oxygenation error (\u3c 1%) is achievable with just 5-7 selected wavelengths. Furthermore, the proposed wavelength selection and estimation algorithms for the wound monitoring application were found to be robust to variations in the peak wavelength and relatively wide bandwidths of the types of LEDs that may be featured in the designs of such devices