大规模景观图像斑块特征增强算法仿真

Simulation of large scale landscape image patch feature enhancement algorithm

不同景观斑块特征存在一定的差异,整体增强会导致斑块重叠和模糊等问题。为此,提出一种大规模景观图像斑块特征增强算法。计算大规模景观图像斑块形状指数、多样性指数、破碎性指数、最大斑块指数以及优势度指数,以此反映景观图像内斑块组成和结构特征,并度量景观斑块特征;再将所有指数计算结果组成斑块特征集,输入多分支注意力机制卷积神经网络中,依据网络通道注意力机制增强图像斑块特征分辨率;最后,将增强结果作为局部特征融合网络的输入,通过该网络的卷积操作生成各个通道的局部斑块图,获取局部特征、斑块特征的位置和细节信息,完成斑块特征二次增强。仿真实验结果表明:所提出的增强算法的梯度损失和结构相似性损失函数值均在0.10以下,说明其能够有效处理斑块边缘之间的模糊效应,并且可靠区分不同的景观斑块分布空间。

There are a certain differences in the characteristics of different landscape patches,and overall enhancement can lead to issues such as patch overlap and blurring.Therefore,a large-scale landscape image patch feature enhancement algorithm is proposed.The shape index,diversity index,fragmentation index,maximum patch index,and dominance index of large-scale landscape image patches are calculated to reflect the composition and structural characteristics of patches in the landscape image,and measure the characteristics of landscape patches.All index calculation results are composed into a patch feature set and input into the multi branch attention mechanism convolutional neural network,so as to enhance the resolution of image patch features by means of the network channel attention mechanism.The enhancement results are used as input of the local feature fusion network,and local patch maps of each channel are generated by means of the convolution operation of the network,obtaining local features,the position and detail information of patch features,and completing the secondary enhancement of patch features.The simulation experimental results show that the gradient loss and structural similarity loss functions of the enhanced algorithm are both below 0.10,indicating that it can effectively handle the fuzzy effects between patch edges and reliably distinguish different landscape patch distribution spaces.