## 1 Introduction
In contrast to the global thresholding method, the local adaptive thresholding method can precisely identify regions of sea ice. Later, [PERSON] et al. [23] achieved commendable experimental results by detecting ice and open water areas in RADARSAT-2 images using adaptive probability thresholding. The early texture analysis method was based on the gray-level co-occurrence matrix (GLCM) method. By calculating the grayscale value differences between pixels in the image, this method assesses the texture characteristics of diverse regions. Unfortunately, the GLCM-based method is computationally intensive and requires significant data pre-processing, making it impractical for large-scale remote sensing of sea ice. To overcome the drawbacks of the GLCM-based method, ref. [24] presented an overview of the one-dimensional discrete wavelet transform method leveraging Daubechies wavelet filters represented by finite vectors and matrices. The method takes into account the scale-dependent wavelet variance and covariance and determines their respective confidence intervals. The wavelet transform-based method can handle large-scale remote sensing images of sea ice more quickly than the GLCM-based method. Numerous machine learning techniques employ hand-crafted feature identification, such as wavelet transform for texture feature identification from images, and using conventional classifiers like support vector machine and decision tree for image segmentation [25]. These methods necessitate manual feature identification, which is subjective and has certain limitations. Advancements in deep learning technology have facilitated the inclusion of machine learning techniques based on convolutional neural networks (CNN) in remote sensing image processing of sea ice. After using substantial remote sensing images and corresponding labelled data to train CNN models, the model is then deployed to categorize new remote sensing images [26]. During the development of machine learning techniques, researchers gradually identify the discrepancy problem between remote sensing image data and ground truth label data. Hence, unsupervised learning methods have emerged, such as generative adversarial networks-based techniques [27; 28]. These techniques can categorize remote sensing images of sea ice, even in the absence of labeled data. Although these three methods have achieved constructive results in the identification of Arctic sea ice, there are still some shortcomings. For example, threshold-based methods are not good at distinguishing thin ice from seawater and are easily affected by light interference. In addition, texture analysis-based methods require classification of different types of sea ice, which requires professional knowledge reserves and experience in acquiring remote sensing images, limiting the technical application scope of such methods. Furthermore, although machine learning methods have achieved good results in image training, they require a large amount of training data and computational resources, as well as the design of professional models, which inevitably consumes a lot of time. In conclusion, the efficient and accurate identification of Arctic sea ice from remote sensing images is a pressing issue. To approach this problem, a constrained energy minimization (CEM) method is considered [29; 30; 31], which solely requires the target spectrum without the need for image background information. By employing predetermined constraints, a finite impulse response filter is developed to enable the desired target signal to pass through, while effectively suppressing output energy introduced by secondary signals. CEM is highly versatile and efficient, making it an ideal approach for numerous remote sensing image processing tasks. It is frequently used for identifying various target objects such as land-cover [32], farmland [33], coastal change [34], and so on [35; 36; 37]. These applications have all yielded impressive results.