To improve the accuracy of indoor localization methods with channel state information (CSI) images, a localization method that used CSI images from selected multiple access points (APs) was proposed. The method had an off-line phase and an on-line phase. In the off-line phase, three APs were selected from the four APs in the localization area based on the received signal strength indication (RSSI). Next, CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card. A single-channel sub-image was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas. These sub-images were then merged to form a three-channel RGB image, which was subsequently fed into the convolutional neural network (CNN) for training. The CNN model was saved upon completion of training. In the on-line phase, the CSI data from the target device was collected, converted into images using the same process as in the off-line phase, and fed into the well-trained CNN model. Finally, the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities. The proposed method was validated in indoor environments using two datasets, achieving good localization accuracy.

The traditional EnFCM (Enhanced fuzzy C-means) algorithm only considers the grey-scale features in image segmentation, resulting in less than satisfactory results when the algorithm is used for remote sensing woodland image segmentation and extraction. An EnFCM remote sensing forest land extraction method based on PCA multi-feature fusion was proposed. Firstly, histogram equalization was applied to improve the image contrast. Secondly, the texture and edge features of the image were extracted, and a multi-feature fused pixel image was generated using the PCA technique. Moreover, the fused feature was used as a feature constraint to measure the difference of pixels instead of a single grey-scale feature. Finally, an improved feature distance metric calculated the similarity between the pixel points and the cluster center to complete the cluster segmentation. The experimental results showed that the error was between 1.5% and 4.0% compared with the forested area counted by experts’ hand-drawing, which could obtain a high accuracy segmentation and extraction result.

In the process of crack detection in subway tunnels, it is difficult to detect tunnel cracks due to the complexity of tunnel environments and the limitation of light conditions. To this effect, a tunnel crack detection method based on multi-feature analysis was proposed. Firstly, the quality of the tunnel crack image was improved by the preprocessing algorithm combining Retinex smoothing and piecewise linear stretching, and then the image was preliminarily segmented by Otsu threshold algorithm for block processing. Secondly, the area and rectangularity of connected domain in the image were analyzed, the linear structural features in the image were extracted by probabilistic Hough transform, and the pseudo crack interference was filtered out by image skeleton feature extraction algorithm. Finally, real crack detection was realized, and the detection rate of traditional crack image and tunnel crack image reached 92% and 86%, respectively. It is experimentally verified that the proposed method is practical and effective.