# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 3 The Proposed Method
### MultiRes-RCF-Based Subject-Sensitive Hashing for HRRS Images
#### 3.2.1 Network Architecture of MultiRes-RCF  
Scale is an important feature of HRRS images. It generally refers to the relative size of the spatial range and the relative length of time expressed by the data. In the analysis and use of HRRS images, there are obvious scale differences in identifying different ground objects [24; 25]. As the same type of features may have different descriptions at different scales, feature extraction at a single scale cannot satisfy all analyses and applications, while multi-scale analysis methods can form a feature set with a hierarchical structure and can obtain more accurate and objective information. The subject-sensitive hash algorithm based on a multi-scale strategy can not only enhance the tamper sensitivity of the algorithm, but also expand the application scope of the authentication algorithm. However, it is difficult to predict at which scale the content of HRRS images can be better detected in most cases. We believe that blockchain-oriented subject-sensitive hashing should be able to detect and identify objects of different scales. On the basis of richer convolutional features (RCF) [26], we propose a network named MultiRes-RCF (richer convolutional features). MultiRes-RCF is based on the MultiRes mechanism [27] to improve the performance of the RCF network to extract multi-scale features of HRRS images. A neat contribution of the RCF architecture is to fully utilize the CNN features from all convolutional layers, making good use of the rich feature hierarchy. The network structure of RCF is built based on the VGG16, and all the fully connected layers and the fifth pooling layer of VGG16 are removed. RCF divide the convolution into five stages, and the pooling layers downsample the two adjacent stages. This enables RCF to automatically combine complementary information from all layers of the CNN to obtain accurate representations of the objects. However, the 3 \(\times\) 3 convolutional layers used by RCF do not have many advantages in extracting multi-scale features. Moreover, as the width and depth of deep neural networks increase, they face the problem of network degradation caused by vanishing gradients or exploding gradients. In MultiResU-Net [27], the sequence of two convolutional layers of the original U-Net [28] is replaced by MultiRes blocks to solve the problem of different scales of images. As shown in Figure 2, a series of 3 \(\times\) 3 convolution kernels are used in the MultiRes block to simulate the receptive fields of 5 \(\times\) 5 convolution kernels and 7 \(\times\) 7 convolution kernels. At the same time, the input of these convolution kernels is directly superimposed with the output of these convolution kernels through the residual path after 1 \(\times\) 1 convolution. This can not only extract image features of different scales but can also reduce the amount of network computation. After all, it is very computationally expensive to directly use 5 \(\times\) 5 convolution kernels and 7 \(\times\) 7 convolution kernels to extract multi-scale features. Our proposed MultiRes-RCF is based on MultiRes blocks to improve the RCF and enhance its ability to extract multi-scale features without significantly increasing the scale and computational complexity of the model. As shown in Figure 3, the network input size of MultiRes-RCF is 256 \(\times\) 256 pixels. MultiRes-RCF consists of 4 stages--1 stage less than the original RCF. This is because each stage of RCF contains 2 or 3 3 \(\times\) 3 convolution kernels, while each stage of MultiRes-RCF contains 2 MultiRes blocks, implementing 3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7 convolution operations to merge in parallel. Except for the output layer, the activation function of all the convolutional layers in this network is the ReLU (Rectified Linear Unit), and each convolutional layer is batch normalized. Figure 3: The structure of our proposed MultiRes-RCF networks. Figure 2: The structure of MultiRes blocks.