Scene classification of remote sensing (RS) images has attracted increasing attention due to its wide applications. Recently, with the advances of deep learning models, especially convolutional neural networks (CNNs), the performance of remote sensing image scene classification has been significantly improved. In this paper, based on the popular CNN, we develop a new scene classification network, named the Global and Local Consistent (GLC) network, to deeply explore useful information from the RS images. First, we adopt a pre-trained CNN to learn the intermediate feature maps from the RS image pairs. Second, by introducing the visual attention mechanism, the global and local integration model is developed to mine the rich information from the obtained feature maps. Third, the attention consistent model is designed to eliminate the negative influence of the issue of attention inconsistency on the classification. To verify the effectiveness of the proposed method, we select two popular RS image data sets. Compared with some existing classification models, our network can achieve competitive results, which illustrates that our method is useul to the RS scene classification.

With the development of deep learning, hyperspectral image (HSI) classification tasks have developed rapidly, the classification performance is improved in a big degree. Despite the great success of the existing methods, there is still room for improvement to extract features from spatial and spectral dimensions. In this paper, we propose a multiscale spatial and spectral feature network (MSSFN) to capture discriminative features for the classification of HSIs. Specifically, we first use three convolution layers to extract the features of original HSI data. Second, combining the spatial masks model and spectral attention model to build multiscale spatial and spectral model (MSSM). Through the MSSM model, the spatial information of different scales can be obtained and the useful spectral bands can be emphasized. Finally, in order to reduce the computation complexity and simplify network, the other three convolution layers with a small number of convolution kernels are adopted in our method. The experimental results demonstrate that our method is superior to most existing methods on two public HSI datasets.

This paper presents a joint dehazing and denoising scheme for an image taken in hazy conditions. Conventional image dehazing methods may amplify the noise depending on the distance and density of the haze. To suppress the noise and improve the dehazing performance, an imaging model is modified by adding the process of amplifying the noise in hazy conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for dehazing the image. The proposed iterative image dehazing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and dehazed image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different days are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme weather conditions without amplifying the noise.

This paper presents a technique that removes specular reflection in an image without sacrificing texture and chromaticity fidelity in diffuse regions of the image. This method is based on the observation that there exist structural similarity both in diffuse and specular components with original highlight image as well as strong local correlation in chromaticity in diffuse component. Local correlation in chromaticity regularizes chromaticity consistency. Structural similarity is measured using a gradient magnitude similarity map of a diffuse region. A diffuse component is estimated by solving local structure and chromaticity joint compensation problem. Experiment results show that the proposed method outperforms state-of-the-art specular reflection removal methods in terms of visual evaluation and the degree of information loss.