Recently, hashing has been successfully applied for large-scale remote sensing image retrieval (LSRSIR) due to its advantage in terms of computation and storage. In LSRSIR, existing hashing methods mainly focus on single-source remotely sensed data. They cannot effectively fuse multisource remotely sensed data, which has a large potential for LSRSIR. To fulfill this gap, this letter proposes a novel deep hashing method, dubbed Multiview Graph Convolutional Hashing (MGCH) that can successfully fuse multisource remote sensing image. Since graph convolutional network (GCN) has been applied as an effective means that expresses and integrates relationships into features, MGCH applies a GCN to explore inherent structural similarity among multiview data, which will help to generate discriminative hash codes. An asymmetric scheme is developed that optimizes the proposed deep model in an end-to-end manner to improve training efficiency. We evaluate the proposed method by fusing two different kinds of RS images, i.e., multispectral (MUL) image and panchromatic (PAN) image. The experimental results on the dual-source RS image data set (DSRSID) show that the proposed MGCH outperforms state-of-the-art multiview hashing methods.

Convolutional neural networks (CNNs), a kind of feedforward neural network with a deep structure, are one of the representative methods in hyperspectral image (HSI) classification. However, redundant information and interclass interference are common and challenging problems in HSI classification. In addition, if the spectral and spatial information is not properly extracted and analyzed, it will affect the classification performance of the network to a great extent. Aiming at these issues, this article proposes an HSI classification method based on an adaptive hash attention mechanism and a lower triangular network (AHA-LT). First, the attention mechanism is introduced in the preprocessing stage, which is composed of the spectral attention module and the adaptive hash spatial attention module in series. Then, the data processed by the attention mechanism are introduced into the lower triangular network (LTNet) to obtain the fused high-dimensional semantic features. Finally, we compress the features and obtain the output classification results through several fully connected layers. Among them, LTNet is composed of 2-D–3-D CNN and multiscale features. The network integrates the characteristics of multibranch, feature fusion, feature compression, and skip connections. Extensive experiments on four widely used HSI data sets show that the proposed method can obtain a great improvement in performance compared with the existing methods.

68Ga-DOTATATE PET-CT is routinely used for imaging neuroendocrine tumor (NET) somatostatin receptor subtype 2 (SSTR2) density in patients, and is complementary to FDG PET-CT for improving the accuracy of NET detection, characterization, grading, staging, and predicting/monitoring NET responses to treatment. Performing sequential18F-FDG and68Ga-DOTATATE PET scans would require 2 or more days and can delay patient care. To align temporal and spatial measurements of18F-FDG and68Ga-DOTATATE PET, and to reduce scan time and CT radiation exposure to patients, we propose a single-imaging session dual-tracer dynamic PET acquisition protocol in the study. A recurrent extreme gradient boosting (rXGBoost) machine learning algorithm was proposed to separate the mixed18F-FDG and68Ga-DOTATATE time activity curves (TACs) for the region of interest (ROI) based quantification with tracer kinetic modeling. A conventional parallel multi-tracer compartment modeling method was also implemented for reference. Single-scan dual-tracer dynamic PET was simulated from 12 NET patient studies with18F-FDG and68Ga-DOTATATE 45-min dynamic PET scans separately obtained within 2 days. Our experimental results suggested an18F-FDG injection first followed by68Ga-DOTATATE with a minimum 5 min delayed injection protocol for the separation of mixed18F-FDG and68Ga-DOTATATE TACs using rXGBoost algorithm followed by tracer kinetic modeling is highly feasible.

Spatial-spectral classification is an important research direction in hyperspectral image (HSI) processing, but HSI generally cannot provide enough spatial information because of its lower spatial resolution. To improve the classification accuracy on HSI, we propose an adaptive correction strategy on a cooperative classification framework under the assist of multispectral image (MSI), and this strategy adaptively selects the most suitable guided filter to do decision correction for each category. Experimental results demonstrate the effectiveness of the proposed method on cooperative classification.

This paper presents an interactive image segmen-tation algorithm, in which the segmentation problem is formulated as a global manifold learning process. Based on the principle that the label of each element depends on the influence of all the elements in the image, we extend the conventional local neighborhood or the long range regional relationships to the global relationships over the whole image. A probabilistic framework is established to measure the global effect of each element on all other elements. Based on two different manifold learning styles, the semi-global manifold learning (SGML) and the fully-global manifold learning (FGML) algorithms are proposed to capture the global geometry structure of the data manifold. SGML learns the intrinsic effects of each element separately, equivalent to a matrix diffusion process on an affinity graph. FGML learns the intrinsic effects of all elements together, equivalent to a label pair diffusion process on a higher-order tensor product graph. The global manifold learning helps to overcome the low contrast, weak boundary and texture problems. Extensive experiments on three public interactive segmentation datasets demonstrate the superior performance of the proposed algorithms both in accuracy and efficiency.

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized sub-problems. The sub-problems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

Random noise in hyperspectral images (HSIs) may significantly degrade the image quality and further affect the subsequent image applications, such as land cover classification. To improve the performance of the existing classification methods on noisy HSIs, we propose a framework to take full advantages of the superpixel segmentation and the traditional pixel-wise classification methods. First, a novel superpixel model is proposed for HSI segmentation, where a new spectral similarity is defined in wavelet domain to make the superpixel model more robust to random noise; then, a simple but effective fusion strategy is designed to combine the superpixels with the pixel-wise classification results. Experimental results demonstrate the effectiveness of the proposed superpixel model and fusion strategy on noisy HSIs.

Fully polarimetric synthetic aperture radar (PolSAR) images are generally expressed as the complex-valued (CV) matrix, whereas the convolutional neural networks (CNNs) have been successfully utilized for the PolSAR image classification. However, most 2D or 3D CNNs suffer from insufficient exploring the CV features or computationally expensive. To address these issues, this paper presents an efficient complex-valued spatial-scattering separated attention network (CVS3ANet) for PolSAR image classification. The CVS3ANet utilizes CV-3D convolutions to explore the features in both spatial and scattering dimensions of the PolSAR images, and reduces the parameters by factorizing the 3D convolution as a sequential process of 2D spatial convolution followed by 1D scattering convolution. Moreover, a squeeze and fusion attention unit is used to enhance the learning interpretation ability of the network by modeling correlations between channels with respect to attention probability. The experimental results demonstrate that the proposed method can obtain superior results over the state-of-the-art techniques.

Accurate estimation of noise level in images plays an important role in different image processing applications. The current algorithms can precisely estimate noise with smooth images, but it is still the challenge to approximate noise level from richly textured images. In this paper, we proposed a new adaptive superpixel classification algorithm for noise estimation in complicated textured images. Firstly, our new superpixel algorithm adapts the finite Gaussian clustering approach, which can better approximate homogeneous patches in noisy images. Then noise information is obtained locally from each superpixel patch. Finally, the best estimation of noise level is calculated with a statistical approach. Experimental results with various kinds of images demonstrate that our method is more accurate and robust compared to the five existing common used algorithms.