## 3 Method  
### Construction of the New MRMDL Model  
Based on the heterogeneity of inputs data, the architecture of the new MRMDL model suggested in this work is primarily composed of MResNet and MHL-NN. MResNet is utilized to identify effective features from the combination of BRCS DDM and Raw DDM. MHL-NN is employed for SSH retrieval. (1) The design of MResNet structure  
In this section, the new MResNet model is built to identify the effective features of DDM. CNN is a neural network designed for image data and composed primarily by convolutional and pooling layers [38; 39]. The convolution layer identifies features and eliminates noise by convolving the input data; the pooling layer downsamples the feature map to retain the main features [40]. The depth of the CNN model has a crucial effect on the modelling performance. It is widely assumed that the more layers of convolution and pooling layers used, the more complete the information on image characteristics is collected. Unfortunately, in practical experiments, with the superposition of convolutional and pooling layers, problems such as gradient disappearance (or explosion), overfitting, and degradation problems emerged. The problem of gradient disappearance (or explosion) can be solved by preprocessing the data and using batch normalization layers in the network. Overfitting can be optimized by using Data Augmentation, Regularization, and Dropout. The degradation problem is a phenomenon in which as the depth of the network increases, the network accuracy reaches saturation or even decreases (the training error increases as well). This is primarily because a standard CNN model is a non-nested class of functions [41]. Suppose \(\mathcal{F}\) is the set of a particular class of convolutional neural network architectures; \(f^{*}\) is our desired objective function. As shown in Figure 1a, for non-nested function classes, functions in more complex function classes (represented by larger areas) are not guaranteed to be closer to the objective function and may be worse (increasing in complexity from A to B). In contrast, for the nested function class in Figure 1b, the simple function class is always contained within the complex function class. In 2016, [PERSON] et al. proposed Residual Net (ResNet), that effectively solved the degeneracy problem in deep networks and profoundly influenced the design of subsequent deep neural networks [41]. The foundation of the ResNet is the residual blocks, which suggests that each additional layer should more easily contain the original function as its elements. Figure 2 presents a diagram of the regular convolution block and the residual block. It can be seen that the output of the regular convolution block is \(f(x)\), while the output of the residual block not only contains \(f(x)\) but also adds the original input \(x\). When both the weight and bias parameters of \(f(x)\) are 0, the function is a constant mapping; the new function class contains the original function class after increasing the number of layers. After adding the residual structure, the CNN becomes a nested function class model, which solves the degradation problem of the CNN network. Because of the low reflected signal power, high noise, and tiny image size of DDM, the residual structure of ResNet is not suitable for DDM data feature extraction. Therefore, we designed a new modified residual network (MResNet) applicable to the DDM data structure to extract DDM features efficiently. First, the residual block contains two \(3\times 3\) convolutional layers, with each of these layers followed by a batch normalization layer and a ReLU activation function. Second, by skipping these two convolutional procedures in the cross-layer data path, the input is directly appended preceding the final ReLU activation function. For the outputs of the two convolutional layers to be joined together, they must have the same shape as the inputs.