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Multispectral image change detection is an important application in the field of remote sensing. Multispectral images usually contain many complex scenes, such as ground objects with diverse scales and proportions, so the change detection task expects the feature extractor is superior in adaptive multi-scale feature learning. To address the above-mentioned problems, a multispectral image change detection method based on multi-scale adaptive kernel network and multimodal conditional random field (MSAK-Net-MCRF) is proposed. The multi-scale adaptive kernel network (MSAK-Net) extends the encoding path of the U-Net, and designs a weight-sharing bilateral encoding path, which simultaneously extracts independent features of bi-temporal multispectral images without introducing additional parameters. A selective convolution kernel block (SCKB) that can adaptively assign weights is designed and embedded in the encoding path of MSAK-Net to extract multi-scale features in images. MSAK-Net retains the skip connections in the U-Net, and embeds an upsampling module (UM) based on the attention mechanism in the decoding path, which can give the feature map a better expression of change information in both the channel dimension and the spatial dimension. Finally, the multimodal conditional random field (MCRF) is used to smooth the detection results of the MSAK-Net. Experimental results on two public multispectral datasets indicate the effectiveness and robustness of the proposed method when compared with other state-of-the-art methods.

This paper presents a new algorithm of traditional camera calibration which is based on Extended Kalman Filter. The Extended Kalman Filter is a set of mathematical equations that provides an efficient way to estimate the state of a process. The technique of camera calibration can be divided into three kinds in practice, among which the traditional camera calibration has the highest precision. The substance of camera calibration is to figure out the intrinsic and extrinsic parameters of a camera for its further application in computer vision. In this paper, the image coordinates of the characteristics and the corresponding world coordinates are taken as the filter's inputs; the estimate value of these parameters are taken as the filter's outputs. Simulation and experiments results show the well optimization of this method.

Change detection of high‐resolution remote sensing images can help to accurately understand the changes in the earth's surface. Advanced methods based on deep features have some limitations, including limited accuracy, poor detection effect, and poor robustness. The main reason is that these frameworks have poor feature extraction capabilities, insufficient context aggregation, and inadequate discrimination capabilities. In order to solve these problems, SiHDNet, a Siamese segmentation network based on deep, high‐resolution differential feature interaction, is proposed. Specifically, after the high‐resolution features of the dual‐temporal image are extracted, the difference map is generated through a special fusion module, which contains sufficient and effective change information. Finally, the final binary change map is obtained through the improved spatial pyramid pooling module. Experiments are conducted on the newly released building change detection data set LEVIR‐CD and the challenging remote sensing image change detection data set Google Data Set. Five benchmark methods are chosen. The results of quantitative analysis and qualitative comparison show that SiHDNet is superior to the five benchmark methods. The results of the ablation experiment also verify the effectiveness of this method.