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Image dehazing is a useful technique which can eliminate the bad effect of haze on images and enhance the performances of image/video processing algorithms in the hazy weather. In this study, a single image dehazing method is proposed. The authors estimate the initial transmission properly based on latent region-segmentation and refine the estimated initial transmission by an objective function with a novel weighted L 1-norm regularisation term. The half-quadratic splitting minimisation method is employed to solve this optimisation problem. They also define an evaluation function to estimate the reliable global atmospheric light. With the refined transmission map and atmospheric light they recover the haze-free image by the haze imaging model. The authors' method is compared with three state-of-the-art methods and is also validated by two image quality assessment methods. The comparative experimental results and evaluations demonstrate that their method can recover comparable and even better results with clear details, low contrast loss and high contrast in most cases.

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The detection and evaluation of concealed mineral resources deep in metallic mines and in the surrounding areas remain technically difficult. In particular, due to the complex topographic and geomorphic conditions on the surface, the detection environments in these areas limit the choices of detection equipment and data collection devices. In this study, based on metallogenic theory and the metallogenic geological characteristics of banded iron formation (BIF)-type iron ores, equipment for surface geophysical surveys (i.e., the high-precision ground magnetic survey method, the transient electromagnetic method, and the magnetotelluric method) and data collection devices capable of taking single-point continuous measurements were employed to detect the concealed iron ore bodies in the transition zone CID-1 between the Hejia and Dumu iron deposits in the Gongchangling iron ore concentration area in the Anshan-Benxi area (Liaoyang, China), a representative area of BIF-type iron ores. The results showed that an optimal combination of these geophysical survey methods accurately determined the anomalous planar spatial locations and anomalous profile morphologies of the concealed iron ore bodies. On this basis, we determined their locations, burial depths, and scales. Two anomalous zones induced by concealed iron ore bodies, YC-1 and YC-2, were discovered in zone CID-1. Two concealed iron-bearing zones, one shallow (0–150 m) and one deep (300–450 m), were found in YC-1. A 100 m scale drilling test showed that the cumulative thickness of the shallow iron-bearing zone was over 23.6 m.

In the uncooled infrared imaging systems, owing to the non-uniformity of the amplifier in the readout circuit, the infrared image has obvious stripe noise, which greatly affects its quality. In this study, the generation mechanism of stripe noise is analyzed, and a new stripe correction algorithm based on wavelet analysis and gradient equalization is proposed, according to the single-direction distribution of the fixed image noise of infrared focal plane array. The raw infrared image is transformed by a wavelet transform, and the cumulative histogram of the vertical component is convolved by a Gaussian operator with a one-dimensional matrix, in order to achieve gradient equalization in the horizontal direction. In addition, the stripe noise is further separated from the edge texture by a guided filter. The algorithm is verified by simulating noised image and real infrared image, and the comparison experiment and qualitative and quantitative analysis with the current advanced algorithm show that the correction result of the algorithm in this paper is not only mild in visual effect, but also that the structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) indexes can get the best result. It is shown that this algorithm can effectively remove stripe noise without losing details, and the correction performance of this method is better than the most advanced method.

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Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.

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