Land Cover Extraction of Remote Sensing Images with Parallel Convolutional Network

Abstract

As an important link between human and nature, land use/land cover change (LUCC) has become a research hotspot since the first day of the issue initiated. Remote sensing observation supplies important data source for LUCC research. With the constant improvement of the remote sensed image spatial resolution and the continuous development of computer scientific algorithms and models, the study of semantic segmentation in the field of automatic interpretation of remote sensed images has promoted quickly. There have been a number of achievements focusing on the one-class extraction of LUCC classification system, but limited progress in multi-class classifying mission when facing high resolution remote sensing data by using semantic segmentation model, because of its sophisticated structure, the phenomenon of synonyms spectrum and foreign body with spectrum in images. Additionally, the limitation of the semantic segmentation model is a crucial issue which needs to be solv for precision of classification in class recognition and the perfection of semantic segmentation on pixel level as well. The High-Resolution network (HRnet) model was used and improved in the study for semantic segmentation because it has the advantage of low resolution loss. In the LUCC classification work, the distribution of classes (such as cultivated land, woodland, grassland, other land, water, etc.) are usually extremely imbalanced, that leads to the limited classification recognition results of land types when using HRnet. So a combined Loss function of Lovasz Softmax Loss+Cross Entropy Loss was used in HRnet to classify the land cover of remote sensing images, to meet the requirements of high-resolution image classification not only in terms of low resolution loss but also in terms of class recognition. The combined loss function provided the direction of the model, and the appropriate loss function improved the performance of the model when faced with the imbalanced distribution of land classes. The results are as follows: 1) Compared with Deeplab and Unet, HRnet model performed better in segmentation results; 2) HRnet model with combined Loss function Lovasz Softmax Loss+Cross Entropy Loss (HRnet+) had better performance, and its test accuracy PA(Pixel Accuracy) value was 0.9577, MIOU (Mean Intersection over Union) value was 0.8801, and Kappa value was 0.9455. This indicated that the HRnet model with low loss of resolution could achieve better results in the automatic segmentation of high-resolution remote sensing images, and the combined loss function constructed in this study could effectively solve the problem of imbalanced distribution of classes in remote sensing images, and the edge optimization ability was also more significant.