Active deep learning (ADL) presents an appropriate solution for hyperspectral images (HSIs) classification based on domain adaptation (DA) with limited labelled samples in the target domain. But some challenges still exist. First, traditional ADL methods only match the feature distributions between the source and target domains globally without considering the decision boundaries between classes, which makes the ambiguous features near landcover class boundaries and reduces the classification accuracy. Second, in previous ADL settings, a trained classifier is first used to obtain the predictions for the unlabelled data and then a measure is applied to achieve the uncertainty of such a classifier prediction. This two-step approach does not c

Hyperspectral unmixing technique is a conventional approach addressing the mixed pixel issue. In this paper, we present an autoencoder (AE) network that deals with estimating the abundances in hyperspectral images (HSIs) given the endmembers. In the suggested network, the mixed pixel issue in a supervised scenario is investigated since the weights of the decoder are set equal to the endmembers. More importantly, the network is trained by a blend of two celebrated objective functions, mean squared error and spectral angle distance, in order to have both privileges of sensitivity to small errors and being scale invariant. To assure the convenience, the sparsity and physical constraints are imposed on the abundances, and the regul

This paper presents a variational framework to enhance the spatial details of the low-resolution (LR) multispectral (MS) image by the rich spatial information obtained from the panchromatic (Pan) image. The target high-resolution (HR) MS image is estimated through an inverse super-resolution problem, where the LR MS and Pan images are the observations. The LR MS image is modelled by the decimation of the target HR MS image which takes into account the modulation transfer function (MTF) of the MS sensor. In addition, the Pan image is described as a linear combination of the bands of the target HR MS image. A variational pansharpening model is defined according to the image observation models and the total variation (TV) regulari

Pansharpening refers to the fusion of panchromatic (Pan) image and multispectral (MS) image, which are acquired from the same scene. The output of the process is a high-spatial-resolution MS image. However, in most cases, spectral or spatial distortion occurs in the local region. To solve this problem, this paper introduces a detailed injection model, where estimates the model parameters recursively at the full-scale. The proposed method estimates detailed injection coefficients by finding minimum variance-unbiased (MVU) estimator. The superiority of the proposed method is verified via conducting several experiments on the five satellite datasets and comparing the results quantitatively and visually with several existing method

Recently, deep learning approaches, especially convolutional neural networks (CNNs), have been employed for feature extraction (FE) and hyperspectral images (HSIs) classification. The CNN, with all its capabilities, suffers from the input data size and the vast number of parameters, particularly the weights of fully connected (FC) layers. These problems become bottlenecks in real-time systems and cause overfitting in many applications. This paper presents two methods for solving these problems: 1) FE from the input data by applying 3D-Gabor filters, and 2) optimizing the weights of the FC layer based on information theory to decrease the complexity of the FC layer. Traditional 3D-Gabor filters include steerable characteristics that are of i

Pansharpening is an important way of integrating spatial and spectral information in the field of remote sensing. This field uses the complementary and redundant information between multispectral (MS) images and panchromatic (PAN) images to obtain high spectral and high spatial resolution images. Various pansharpening methods have been introduced so far, each one attempting to provide a pansharpened image with the least distortion and maximum preservation of spectral and spatial information. Due to the importance of this issue, there should be methods and indices to evaluate the performance of different pansharpening algorithms and assess the quality of pansharpened images. In this paper, a segmentation-based method for assessing the qualit

Hyperspectral anomaly detection (HAD) is a branch of target detection which tries to locate pixels that are spectrally or spatially different from their background. In this paper, a visual attention approach is developed to leverage HAD. Traditional HAD methods often try to locate anomalous pixels based on spectral information. However, the spatial features of hyperspectral datasets provide valuable information. Here, we aim to fuse spatial and spectral anomaly features based on bottom-up (BU) and top-down (TD) visual attention mechanisms. Owe to the BU attention, spatial features are extracted by mimicking the primary visual cortex neurons functionality. Also, spectral information is obtained throughout a deep neural network that imitating

Hyperspectral images (HSIs) have a cube form containing spatial information in two dimensions and rich spectral information in the third one. The high volume of spectral bands allows discrimination between various materials with high details. Moreover, by utilizing the spatial features of image such as shape, texture and geometrical structures, the land cover discrimination will be improved. So, fusion of spectral and spatial information can significantly improve the HSI classification. In this work, the spectral-spatial information fusion methods are categorized into three main groups. The first group contains segmentation based methods where objects or super-pixels are used instead of pixels for classification or the obtained segmentation

In this paper, a patch-wise manner based on the sparsity is proposed to fuse a panchromatic (PAN) image and a low resolution multispectral (LMS) image. In the sparsity-based pansharpening methods, improving the training process of the dictionaries and sparse coefficients of the fused image, which is the main goal of this paper, have a significant impact on the fused results. In this paper, the fused image is obtained by minimizing the cost function which is obtained from incorporating a Markov random field (MRF)-based prior model into the maximum a posteriori (MAP) estimation. The contribution of this paper is twofold derived from our proposed prior model. 1) The prior model only involves the parts of the PAN information related to a consid

Spatial information such as texture and shape features as well as spatial contextual information play a key role in representation and analysis of hyperspectral images. Spatial information improves the classification accuracy and addresses the common problem of pixel-wise classification methods, i.e. limited training samples. In this article, a new combination of spectral, texture and shape features, as well as, contextual information in the probabilistic framework is proposed. The texture features are extracted utilizing Gabor filters and the shape features are represented by morphological profiles. The spectral, texture and shape features are separately fed into a probabilistic support vector machine classifier to estimate th

Hyperspectral images (HSIs) have a cube form containing spatial information in two dimensions and rich spectral information in the third one. The high volume of spectral bands allows discrimination between various materials with high details. Moreover, by utilizing the spatial features of image such as shape, texture and geometrical structures, the land cover discrimination will be improved. So, fusion of spectral and spatial information can significantly improve the HSI classification. In this work, the spectral-spatial information fusion methods are categorized into three main groups. The first group contains segmentation based methods where objects or super-pixels are used instead of pixels for classification or the obtained segmentation

Active learning (AL) represents an encouraging solution for hyperspectral image classification based on domain adaptation (DA) with very limited labeled samples in target domain. Although the traditional AL methods have exhibited the promising results in DA, some challenges still exist. On the one hand, the previous AL schemes assign a label to the most informative unlabeled data by user and, thus, are characterized by errors, time, and costs, which ignores dealing with noisy and complex data in target domain. On the other hand, the traditional AL methods based on kernel prediction model assume a predefined kernel and the identical distribution for source and target domains, which reduces the performance of classifier on target domain. To o

Hyperspectral unmixing (HSU) is an essential technique that aims to address the mixed pixels problem in hyperspectral imagery via estimating the abundance of each endmember at every pixel given the endmembers. This article introduces two approaches intending to solve the challenge of the mixed pixels using deep convolutional autoencoders (DCAEs), namely pixel-based DCAE, and cube-based DCAE. The former estimates abundances with the help of only spectral information, while the latter utilizes both spectral and spatial information which results in better unmixing performance. In the proposed frameworks, the weights of the decoder are set equal to the endmembers in order to address the issue in a supervised scenario. The proposed frameworks ar