# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 1 Introduction  
A prominent application of remote sensing (RS) imagery is land use and land cover (LULC) classification. Traditional classification methods often fail to fully exploit the geometric information in remote sensing images, although texture features are sometimes utilized to supplement the spectral characteristics of such images [1]. There are a plethora of approaches to analyzing remote sensing images, including machine learning-based methods [2, 3], such as Support Vector Machines (SVMs) [4] and Random Forests (RFs) [5], as well as deep learning methods like Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs). Remote sensing images are rich in information, necessitating classification methods that are both efficient and yield precise results. The advent of deep learning has effectively addressed this issue. The use of deep learning for the analysis of remote sensing images is becoming increasingly widespread across various research fields, such as perimeter mapping, ecosystem services [6, 7], delineation of agricultural fields [8], large-scale mapping of tree crops [9], extensive road extraction [10], and detection of burned areas [11] Deep learning has also demonstrated high performance in classifying and extracting the necessary information from remote sensing images, as evidenced by achievements with networkmodels like U-Net [12], Residual Networks (ResNet) [13], Deep Residual U-Net (ResU-Net) [14], convolutional network-based semantic segmentation [15], and Pyramid Scene Parsing Network (MP-Net) [16]. Existing image datasets are predominantly composed of three-channel RGB images formulated for computer vision tasks with single-label categorization. Standard CNN architectures are primarily tailored for processing these RGB images and lack the capability to effectively exploit the complex interrelationships among multispectral channels. When applied to remote sensing imagery, these models exhibit deficiencies, including but not limited to inadequate handling of the inherent high-dimensional characteristics of hyperspectral images and challenges in addressing the spectral and spatial correlations within these images. Consequently, conventional CNNs are insufficient for remote sensing tasks. [PERSON] provided a multi-label, multi-resolution dataset named BigEarthNet, comprising 590,326 images, each annotated with multiple labels from the CORINE Land Cover database [17]. In the context of LULC scene classification tasks, [PERSON] et al. demonstrated that models pre-trained with contrastive learning on BigEarthNet outperformed those pre-trained on ImageNet [18]. [PERSON] et al. proposed a triplet sampling strategy utilizing BigEarthNet data for learning high-quality feature representations for content retrieval [19]. [PERSON] and [PERSON] employed contrastive multiview coding for self-supervised pre-training [20]. [PERSON] et al. introduced a self-supervised learning method for satellite imagery, utilizing BigEarthNet labels for LULC scene classification, achieving significant results with their initialization approach [21]. In the application of remote sensing classification, Convolutional Neural Networks (CNNs) possess powerful feature extraction and model generalization capabilities, rapidly advancing in the field of computer vision, with a plethora of CNN-based models being employed for remote sensing image classification. [PERSON] et al. presented a novel framework composed of a deep learning architecture, ResUNet-a, coupled with a new loss function based on the Dice coefficient, which achieved remarkable convergence characteristics [22]. However, the model may encounter blurring or incorrect segmentation in complex scenes. [PERSON] et al. explored methods for quantifying the bias in DNNs for land usage, implementing DNN-based models through fine-tuning existing pre-trained models for school building recognition [23], but the accuracy and bias analysis results might not generalize to other regions or countries.