Multiple instance learning (MIL) effectively solves the inaccurate labeled hyperspectral target detection problems, which models the region containing the target as a positive bag, and the area without the target as a negative bag. However, the real labels of instances in the positive bag are unknown, which increases the difficulty of hyperspectral target detection. In this paper, we propose a hyperspectral target detection method based on Co-teaching multiple instance neural network (Coteaching MINN), in which the positive instances in the positive bags are selected to participate in the training of the network by comparing the loss value of the training data. Furthermore, deterministic annealing (DA) algorithm is proposed to enhance the stability of the network during the training process. The experimental results on simulated data and real hyperspectral data demonstrate the advancement and robustness of the proposed method.

Attention-based deep multiple-instance learning (MIL) has been applied to many machine-learning tasks with imprecise training labels. It is also appealing in hyperspectral target detection, which only requires the label of an area containing some targets, relaxing the effort of labeling the individual pixel in the scene. This article proposes an L1 sparsity-regularized attention multiple-instance neural network (L1-attention MINN) for hyperspectral target detection with imprecise labels that enforces the discrimination of false-positive instances from positively labeled bags. The sparsity constraint applied to the attention estimated for the positive training bags strictly complies with the definition of MIL and maintains better discriminative ability. The proposed algorithm has been evaluated on both simulated and real-field hyperspectral (subpixel) target detection tasks, where advanced performance has been achieved over the state-of-the-art comparisons, showing the effectiveness of the proposed method for target detection from imprecisely labeled hyperspectral data.

Non-invasive heart rate estimation is of great importance in daily monitoring of cardiovascular diseases. In this paper, a bidirectional long short term memory (bi-LSTM) regression network is developed for non-invasive heart rate estimation from the ballistocardiograms (BCG) signals. The proposed deep regression model provides an effective solution to the existing challenges in BCG heart rate estimation, such as the mismatch between the BCG signals and ground-truth reference, multi-sensor fusion and effective time series feature learning. Allowing label uncertainty in the estimation can reduce the manual cost of data annotation while further improving the heart rate estimation performance. Compared with the state-of-the-art BCG heart rate estimation methods, the strong fitting and generalization ability of the proposed deep regression model maintains better robustness to noise (e.g., sensor noise) and perturbations (e.g., body movements) in the BCG signals and provides a more reliable solution for long term heart rate monitoring.

Modeling target detection problem given inaccurate annotations as a multiple instance learning (MIL) problem is an effective way for addressing the ground truth uncertainties of remotely sensed hyperspectral imagery. In this paper, we propose a hyperspectral target detection method based on 1D convolution neural network (1DCNN) feature extraction and long short term memory network (LSTM) under the MIL framework, where the LSTM features for each hyperspectral pixel is further refined by a scoring network as to discriminate the real target instance from the inaccurately labeled hyperspectral regions. The proposed method has achieved superior results on both simulated data and real hyperspectral data over the state-of-the-art methods, showing the prospects for further investigation.

Multiple instance learning (MIL) can be used for solving the imprecisely labeled hyperspectral target detection problems, which only needs the label of an area containing some targets. Furthermore, existing methods decompose this task into a target signature learning task and a follow-on similarity measurement between the estimated signature and the test points. In this paper, we propose a deep multiple instance learning method based on self-attention mechanism, in which the max operation and 1D convolution neural network (1D CNN) are adopted to realize an end-to-end hyperspectral target detection structure without learning the target signature. In the proposed deep MIL target detection method, self-attention mechanism with max operation has advantage in estimating the labels of instances from the positive bag via calculating the contribution of each instance to the bag-level classification. The simulated and real hyperspectral target detection experiments are shown to illustrate the performance of the method.

Topic models [e.g., probabilistic latent semantic analysis, latent Dirichlet allocation (LDA), and supervised LDA] have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership LDA (PM-LDA) model and an associated parameter estimation algorithm. This model can be useful for imagery, where a visual word may be a mixture of multiple topics. Experimental results on visual and sonar imagery show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability previous topic modeling methods do not have.

Topic models (e.g., pLSA, LDA, SLDA) have been widely used for segmenting imagery. These models are confined to crisp segmentation. Yet, there are many images in which some regions cannot be assigned a crisp label (e.g., transition regions between a foggy sky and the ground or between sand and water at a beach). In these cases, a visual word is best represented with partial memberships across multiple topics. To address this, we present a partial membership latent Dirichlet allocation (PM-LDA) model and associated parameter estimation algorithms. Experimental results on two natural image datasets and one SONAR image dataset show that PM-LDA can produce both crisp and soft semantic image segmentations; a capability existing methods do not have.

The aim of this work is to characterize the seafloor by estimating invariant sand ripple parameters from synthetic aperture sonar (SAS) imagery. Using a hierarchical Bayesian framework and a known sensing geometry, a method for estimating sand ripple frequency, amplitude, and orientation values from a single SAS image, as well as from sets of SAS imagery over an area, is presented. This is accomplished through the development of an extended model for sand ripple characterization and a Metropolis-within-Gibbs sampler to estimate sand ripple frequency, amplitude, and orientation characteristics for multiaspect high-frequency side-look sonar data. Results are presented on synthetic and measured SAS imagery that indicate the ability of the proposed method to estimate desired sand ripple characteristics.