# A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
## 1 Introduction
[27] developed a superpixel-wise, multi-scale model for hyperspectral image classification, enhancing robustness to noise. Likewise, [PERSON] et al. [28] improved traffic sign recognition by integrating multi-scale mechanisms that enriched spatial representations across object classes. The conceptual roots of multi-scale learning trace back to classical computer vision techniques, such as Gaussian and Laplacian pyramids, which inspired modern hierarchical models. For instance, Feature Pyramid Networks (FPNs) [29] integrate features across multiple resolutions to improve object detection and segmentation. These strategies have since evolved and been adapted to multimodal and temporal data contexts. [PERSON] et al. [30] proposed a fusion framework for Visual Question Answering (VQA) that integrates multi-scale linguistic features at the word, phrase, and sentence levels. [PERSON] et al. [31] developed a spatio-temporal fusion method based on multi-scale feature extraction to capture structural features in dynamic visual data. [PERSON] et al. [32] developed an attention-guided multi-scale approach for lung cancer detection in CT scans. In image denoising, [PERSON] et al. [33] introduced a residual-based multi-scale network that effectively suppresses noise in CT imagery. For image fusion tasks, [PERSON] et al. [34] employed deep networks to learn shared multi-scale representations from multimodal images, facilitating more coherent and accurate fusion. In geophysical imaging, the SR-RDFAN-LOG network [35] used residual dense and attention-based multi-scale modules to enhance ultrasonic logging image resolution, an essential component in oil and gas exploration. The benefits of multi-scale processing are not limited to the visual domain. In speech emotion recognition, [PERSON] et al. [36] multi-scale CNN blocks to extract richer temporal patterns, resulting in improved classification performance. Text detection in complex environments has also benefited from multi-scale strategies. EMANet [37], for example, achieved high accuracy and speed in digitizing ancient texts for IoT systems through enhanced feature extraction and scale fusion. In medical imaging, multi-scale methods continue to play a pivotal role. MEF-UNet [38] addressed challenges such as low contrast and blurred boundaries in ultrasound image segmentation through selective feature extraction and multi-scale fusion. A more recent vessel segmentation method [39] for retinal fundus images combined multi-scale feature learning with disentangled representation techniques, leveraging dilated convolutions, channel attention at skip connections, and an image reconstruction branch to separate informative content from background noise. This approach achieved state-of-the-art performance on several benchmarks, further underscoring the power of multi-scale processing in clinical diagnostics. In the domain of remote sensing change detection, a novel framework [40] combined multi-scale feature extraction with specialized interaction and guidance modules to enhance edge preservation and semantic difference detection between bitemporal images--effectively addressing issues like seasonal variation and limited ground truth. For urban structure analysis, ME-FCN [41] incorporated tailored multi-scale fusion modules to improve building footprint extraction in complex cityscapes. Additionally, probabilistic Latent Semantic Hashing (pLSH) [42] leveraged multi-scale representations to learn topic-aware binary encodings for large-scale remote sensing image retrieval, demonstrating superior performance in unsupervised settings. Similarly, in Earth observation, a multi-scale feature extraction network [43] was designed to exploit complementary spatial, directional, and spectral cues from multi-source remote sensing data, improving classification accuracy in heterogeneous landscapes.