Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, temporal information. They made a plethora of applications feasible for the analysis large areas Earth?s surface. However, significant number factors-such as high dimensions size data, lack training samples, mixed pixels, light-scattering mechanisms acquisition process, different atmospheric geometric distortions-make such data inherently nonlinear complex, which...

The 2013 Data Fusion Contest organized by the Technical Committee (DFTC) of IEEE Geoscience and Remote Sensing Society aimed at investigating synergistic use hyperspectral Light Detection And Ranging (LiDAR) data. data sets distributed to participants during Contest, a imagery corresponding LiDAR-derived digital surface model (DSM), were acquired NSF-funded Center for Airborne Laser Mapping over University Houston campus its neighboring area in summer 2012. This paper highlights two awarded...

This paper reports the outcomes of 2014 Data Fusion Contest organized by Image Analysis and Technical Committee (IADF TC) IEEE Geoscience Remote Sensing Society (IEEE GRSS). As for previous years, IADF TC a data fusion contest aiming at fostering new ideas solutions multisource remote sensing studies. In edition, participants considered multiresolution multisensor between optical acquired 20-cm resolution long-wave (thermal) infrared hyperspectral 1-m resolution. The was proposed as...

Earth observation using multisensor data is drawing increasing attention. Fusing remotely sensed hyperspectral imagery and light detection ranging (LiDAR) helps to increase application performance. In this article, joint classification of LiDAR investigated an effective hierarchical random walk network (HRWN). the proposed HRWN, a dual-tunnel convolutional neural (CNN) architecture first developed capture spectral spatial features. A pixelwise affinity branch relationships between classes...

Hyperspectral image (HSI) enjoys great advantages over more traditional types for various applications due to the extra knowledge available. For nonideal optical and electronic devices, HSI is always corrupted by noises, such as Gaussian noise, deadlines, stripings. The global correlation across spectrum (GCS) nonlocal self-similarity (NSS) space are two important characteristics HSI. In this paper, a low-rank regularized CANDECOMP/PARAFAC (CP) tensor decomposition (NLR-CPTD) proposed fully...

Hyperspectral image super-resolution by fusing high-resolution multispectral (HR-MSI) and low-resolution hyperspectral (LR-HSI) aims at reconstructing high resolution spatial-spectral information of the scene. Existing methods mostly based on spectral unmixing sparse representation are often developed from a low-level vision task perspective, they cannot sufficiently make use spatial priors available higher-level analysis. To this issue, paper proposes novel HSI method that fully considers...

Existing methods for tensor completion (TC) have limited ability characterizing low-rank (LR) structures. To depict the complex hierarchical knowledge with implicit sparsity attributes hidden in a tensor, we propose new multilayer sparsity-based decomposition (MLSTD) (LRTC). The method encodes structured of by multiple-layer representation. Specifically, use CANDECOMP/PARAFAC (CP) model to decompose into an ensemble sum rank-1 tensors, and number components is easily interpreted as...

With the recent development of joint classification hyperspectral image (HSI) and light detection ranging (LiDAR) data, deep learning methods have achieved promising performance owing to their locally sematic feature extracting ability. Nonetheless, limited receptive field restricted convolutional neural networks (CNNs) represent global contextual sequential attributes, while visual transformers (VITs) lose local semantic information. Focusing on these issues, we propose a fractional Fourier...

We propose a novel semisupervised local discriminant analysis method for feature extraction in hyperspectral remote sensing imagery, with improved performance both ill-posed and poor-posed conditions. The proposed combines unsupervised methods (local linear supervised (linear analysis) framework without any free parameters. underlying idea is to design an optimal projection matrix, which preserves the neighborhood information inferred from unlabeled samples, while simultaneously maximizing...

Nowadays, we have diverse sensor technologies and image processing algorithms that allow one to measure different aspects of objects on the Earth [e.g., spectral characteristics in hyperspectral images (HSIs), height light detection ranging (LiDAR) data, geometry technologies, such as morphological profiles (MPs)]. It is clear no single technology can be sufficient for a reliable classification, but combining many them lead problems curse dimensionality, excessive computation time, so on....

Conventional tensor completion (TC) methods generally assume that the sparsity of tensor-valued data lies in global subspace. The so-called prior is measured by nuclear norm. Such assumption not reliable recovering low-rank (LR) data, especially when considerable elements are missing. To mitigate this weakness, article presents an enhanced model for LRTC using both local and information a latent LR tensor. In specific, we adopt doubly weighted strategy norm along each mode to characterize...

Remote sensing using multisensor platforms has been systematically applied for monitoring and optimizing human activities. Several advanced techniques have developed to enhance extract the spatially spectrally semantic information in hyperspectral image (HSI) light detection ranging (LiDAR) data processing analysis. However, an abundance of redundant sometimes a lack discriminative features reduce efficiency effectiveness multisource classification methods. This article proposes fractional...

Abstract Most unsupervised or semisupervised hyperspectral anomaly detection (HAD) methods train background reconstruction models in the original spectral domain. However, due to noise and spatial resolution limitations, there may be a lack of discrimination between backgrounds anomalies. This makes it easy for autoencoder capture low‐level features shared two, thereby increasing difficulty separating anomalies from backgrounds, which runs counter purpose HAD. To this end, authors map...

When using morphological features for the classification of high resolution hyperspectral images from urban areas, one should consider two important issues. The first is that classical openings and closings degrade object boundaries deform shapes. Morphological by reconstruction can avoid this problem, but process leads to some undesirable effects. Objects expected disappear at a certain scale remain present when reconstruction. second profiles (MPs) with different structuring elements range...

Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due to its significance improving the performance for object detection and classification. In this paper, we propose a joint spectral spatial low-rank (LR) regularized method denoising, based on assumption that free-noise component observed signal can exist latent low-dimensional structure while does not have property. The proposed denoising only considers traditional LR property across domain but also...

This letter introduces a new spectral-spatial classification method for hyperspectral images. A multiscale superpixel segmentation is first used to model the distribution of classes based on spatial information. In this context, original image integrated with maps via feature fusion process in different scales such that pixel-level data can be represented by superpixel-level (MSP) sets. Then, subspace-based support vector machine (SVMsub) adopted obtain inputs. Finally, result achieved...

Intersections are important components of road networks, which critical to both route planning and path optimization. Most existing methods define the intersections as locations where users change their moving directions identify from GPS traces through analyzing users’ turning behaviors. However, these suffer finding an appropriate threshold for direction change, leading true being undetected or spurious falsely detected. In this paper, defined that connect three more segments in different...

Spectral-spatial framework has been widely applied for hyperspectral image classification task. Some well-established models, such as group sparse representation (GSR), have gained a certain advance but still mainly focus on the usage of local spatial similarity and neglect nonlocal information. Recently, self-similarity (NLSS) exploited to support coherence tasks. However, current NLSS-based methods are biased toward direct use information whole, while underlying spectral is not well...

Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep architecture fusion might not always perform better than single source, especially the of hyperspectral and light detection ranging (LiDAR) remote sensing tree species mapping in complex, closed forest canopies. In this paper, we propose a new framework integrate complementary information from LiDAR mapping. We also investigate either "single-band" or multi-band (i.e., fullwaveform) with...

Hyperspectral image compressive sensing reconstruction (HSI-CSR) is an important issue in remote sensing, and has recently been investigated increasingly by the sparsity prior based approaches. However, most of available HSI-CSR methods consider spatial spectral vector domains via vectorizing hyperspectral cubes along a certain dimension. Besides, previous works, little attention paid to exploiting underlying nonlocal structure domain HSI. In this paper, we propose tensor sparse low-rank...

This article presents the scientific outcomes of 2022 Hyperspectral Pansharpening Challenge organized by 12th IEEE Workshop on Image and Signal Processing: Evolution in Remote Sensing (IEEE WHISPERS 2022). The aims at fusing a panchromatic image with hyperspectral data to get high spatial resolution cube same while preserving spectral information data. Four datasets acquired PRISMA mission owned managed Italian Space Agency have been prepared for participants. They are made available benefit...

This paper presents an optimized kernel minimum noise fraction transformation (OKMNF) for feature extraction of hyperspectral imagery. The proposed approach is based on the (KMNF) transformation, which a nonlinear dimensionality reduction method. KMNF can map original data into higher dimensional space and provide small number quality features classification some other post processing. Noise estimation important component in KMNF. It often estimated strong relationship between adjacent...

This paper proposes a method to combine feature fusion and decision together for multi-sensor data classification. First, morphological features which contain elevation spatial information, are generated on both LiDAR the first few principal components (PCs) of original hyper-spectral (HS) image. We got fused by projecting spectral (original HS image), onto lower subspace through graph-based method. Then, we four classification maps using features, graph individually as input SVM classifier....