# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 1 Introduction
[PERSON] and [PERSON] utilized a multi-attention strategy employing bidirectional long short-term memory networks to capture and leverage the spectral and spatial information content of RS images [24], employing complex multi-branch CNNs and multi-attention mechanisms, which resulted in high computational complexity. [PERSON] et al. proposed an oversampling approach to address the issue of class imbalance in BigEarthNet's land use/cover categories [25], but an excessive focus on minority classes could lead the model to perform well on these while underperforming in other classes. [PERSON] et al. used the Dilated-ResUnet deep learning architecture for extracting buildings and small objects [26], with the proposed model performing well on the FCC (NIR, Red, Green) dataset, but its performance may decrease on other spectral band combinations. Among a plethora of models, we have discerned that the residual learning framework introduced by ResNet facilitates the simplification of training processes for otherwise challenging deep networks. Furthermore, the deep structure of ResNet is capable of learning a broad spectrum of features ranging from generic to specific, which proves to be extremely beneficial for classifying the diverse categories present within remote sensing data. The block structure of ResNet also permits easy modifications and expansions of the network to meet the specific requirements of the task at hand. [PERSON] et al. have proposed a deep architecture endowed with a Region Proposal Network (RPN) that exploits the texture and edges of images to extract pertinent regions by sliding a network over feature maps extracted from deep architectures, namely VGGNet and ResNet, where the deep capabilities of ResNet have exhibited commendable performance [27]. [PERSON] et al. have introduced a novel 51-layer TCNN (ResNet-50) for fault diagnosis, which utilizes the ResNet-50, trained on ImageNet, as a feature extractor for fault diagnosis, outperforming other deep learning models and traditional methods, thus reaffirming the transfer learning capabilities of ResNet [28]. [PERSON] et al. have put forth an image classification methodology based on the ResNet architecture for the detection of colorectal cancer, demonstrating favorable classification performance. ResNet has also produced highly reliable and reproducible results in biomedical image analysis [29]. [PERSON] et al. have proposed a COVID-ResNet auxiliary diagnostic model based on CT images, where the squeeze-and-excitation mechanism of ResNet's residual connections, coupled with the easily modifiable and expandable block structure of ResNet, has resulted in enhanced performance [30]. [PERSON] et al. have introduced an attention-based adaptive spectral-spatial kernel improved residual network (A2S2K-ResNet), incorporating an attention mechanism and refining the residual modules of ResNet with three-dimensional ResBlocks to jointly extract spectral-spatial features, yielding impressive classification outcomes [31]. The feature fusion capability of the ResNet model, which integrates layers or modules that combine spatial and spectral features, along with its customized classification head, exhibits clear advantages in the design of multi-label classification. To address the complex task of classifying remote sensing imagery applications, this paper introduces MMDL-Net, a multispectral, multi-label classification model designed for high-resolution remote sensing imagery. It incorporates a multispectral stacking module, a multi-label classification module, and a feature extraction module that combines Residual Networks (ResNets) with TensorFlow. This module captures global information as base-level features from feature maps, and by increasing the number of channels, it ensures the transfer of more detailed information, providing an expanded feature representation space. The network's input layer and fully connected layer are further designed to enhance the network's robustness to changes and noise in the input images, thereby improving classification accuracy.