Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general ...applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.
Hyperspectral image (HSI) involves vast quantities of information that can help with the image analysis. However, this information has sometimes been proved to be redundant, considering specific ...applications such as HSI classification and anomaly detection. To address this problem, hyperspectral band selection is viewed as an effective dimensionality reduction method that can remove the redundant components of HSI. Various HSI band selection methods have been proposed recently, and the clustering-based method is a traditional one. This agglomerative method has been considered simple and straightforward, while the performance is generally inferior to the state of the art. To tackle the inherent drawbacks of the clustering-based band selection method, a new framework concerning on dual clustering is proposed in this paper. The main contribution can be concluded as follows: 1) a novel descriptor that reveals the context of HSI efficiently; 2) a dual clustering method that includes the contextual information in the clustering process; 3) a new strategy that selects the cluster representatives jointly considering the mutual effects of each cluster. Experimental results on three real-world HSIs verify the noticeable accuracy of the proposed method, with regard to the HSI classification application. The main comparison has been conducted among several recent clustering-based band selection methods and constraint-based band selection methods, demonstrating the superiority of the technique that we present.
Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource ...utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.
•We propose a novel alignment method to construct a common feature space under the guidance of a Gaussian prior for UDA.•We introduce a new method to align two distributions by minimizing the direct ...L1-distance between the decoded samples.•The proposed work achieves state-of-the-art performance on both digit and object classification tasks.
In this paper, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. Previous work has attempted to directly align features by classifier-induced discrepancies. Nevertheless, a common feature space cannot always be learned via this direct feature alignment especially when large domain gaps exist. To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of a prior. In such an indirect way, the distributions over the samples from the two domains will be constructed on a common feature space, i.e., the space of the prior, which promotes better feature alignment. To effectively align the target latent distribution with this prior distribution, we also propose a novel unpaired L1-distance by taking advantage of the formulation of the encoder-decoder. The extensive evaluations on nine benchmark datasets validate the superior knowledge transferability through outperforming state-of-the-art methods and the versatility of the proposed method by improving the existing work significantly.
Recent advances on remote sensing techniques allow easier access to imaging spectrometer data. Manually labeling and processing of such collected hyperspectral images (HSIs) with a vast quantities of ...samples and a large number of bands is labor and time consuming. To relieve these manual processes, machine learning based HSI processing methods have attracted increasing research attention. A major assumption in many machine learning problems is that the training and testing data are in the same feature space and follow the same distribution. However, this assumption doesn't always hold true in many real world problems, especially in certain HSI processing problems with extremely insufficient or even without training samples. In this letter, we present a transfer learning framework to address this unsupervised challenge (i.e., without training samples in the target domain), by making the following three main contributions: 1) to the best of our knowledge, this is the first time for transfer learning framework to be used for the classification of totally unknown target HSI data with no training samples; 2) the characteristics of HSI are learned on dual spaces to exploit its structure knowledge to better label HSI samples; and 3) two specific new scenarios suitable for transfer learning are investigated. Experimental results on several real world HSIs support the superiority of the proposed work.
A hyperspectral image (HSI) includes a vast quantity of samples, a large number of bands, and randomly occurring redundancy. Classifying such complex data is challenging, and its classification ...performance can be affected significantly by the amount of labeled training samples, as well as the quality, position, and others factors of these samples. Collecting such labeled training samples is labor and time consuming, motivating the idea of taking advantage of labeled samples from other pre-existing related images. Therefore, transfer learning, which can mitigate the semantic gap between existing and new HSIs, has drawn increasing research attention. However, existing transfer learning methods for HSIs (which mainly concentrate on how to overcome the divergence among images) may fail to carefully consider the contents to be transferred and thus limit their performances. In this paper, we present two novel ideas: 1) we, for the first time, introduce an active learning process to initialize the salient samples on the HSI data, which would be transferred later; and 2) we propose constructing and connecting higher level features for the source and target HSI data to further overcome the cross-domain disparity. Different from existing methods, the proposed framework requires no a priori knowledge on the target domain, and it works for both homogeneous and heterogeneous HSI data. Experimental results on three real-world HSIs support the effectiveness of the proposed method for HSI classification.
Hyperspectral image (HSI) classification is one of the most challenging problems in understanding HSI. Convolutional neural network(CNN), with the strong ability to extract features using the hidden ...layers in the network, has been introduced to solve this problem. However, several fully connected layers are always appended at the end of CNN, which dramatically reduced the efficiency of space utilization and make the classification algorithm hard to converge. Recently, a new network architecture called capsule network (CapsNet) was presented to improve the CNN. It uses groups of neurons as capsules to replace the neurons in traditional neural networks. Since the capsule can provide superior spectral features and spatial information extracted, its performance is better than the most advanced CNN in some fields. Motivated by this idea, a new remote sensing hyperspectral image classification algorithm called Conv-Caps is proposed to make full use of the advantages of both. We integrate spectral and spatial information into the proposed framework and combine Conv-Caps with Markov Random Field (MRF), which uses the graph cut expansion method to solve the classification task. The Caps-MRF method is further proposed. First, select an initial feature extractor,which a CNN without fully connected layers. Then, the initial recognition feature map is put into the newly designed CapsNet to obtain the probability map. Finally, the MRF model is used to calculate the subdivision labels. The presented method is trained with three real HSI datasets and is compared with the latest methods. We find the framework can produce competitive classification performance.
A growing number of state-of-the-art crowd counting methods employ the regression model. Such a model learns a person-density map first and its integral is further calculated to obtain the final ...count. However, this learned density map is uninterpretable and could deviate largely from the true person distribution even when the final count is accurate. In comparison, we present a conceptually interpretable and technically simple classification model for crowd counting, which consists of three novel modules: Deep Integrated Module (DIM), Scale Adaptive Module (SAM), and Interval Aware Module (IAM). Different from the traditional density map, the proposed pedestrian-aware density map (PADM) in our model can reveal the true people density, and meanwhile tackle the rarely-explored crowd localization task simultaneously. The proposed joint crowd counting and localization method does not require extra pretraining or fine-tuning for individual components of the network, and we train our model end-to-end in a single step. Without bells and whistles but a few lines of code, our simple yet effective method achieves better performances on both crowd counting and localization tasks when compared with state-of-the-art methods. The code is available online.
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