No abstract

Accurate and efficient semantic segmentation of buildings in high spatial resolution (HSR) remote sensing images is the basis for applications such as fine urban management, high-precision mapping, land resource utilization investigation, and human settlement suitability evaluation. The current building extraction methods based on deep learning can obtain high-level abstract features of images. However, due to the limitation of convolution kernel size and the vanishing gradient, the extraction of some buildings is inaccurate, and some small-volume buildings are missing as the network deepens. In this regard, we design a horizontally connected residual blocks-based multi-scale attention network (HCRB-MSAN) to achieve high-quality extraction of buildings in HSR remote sensing image. In this network, we subdivide each residual block by channel grouping and feature horizontal connection to consider the difference and saliency of feature information between channels, and then combine the output features with multi-scale attention module to consider the contextual semantic relationship of different regions and integrate multi-level local and global information of buildings. A stepwise up-sampling mechanism is designed in the decoding process to finally achieve precise semantic segmentation of buildings. We conduct experiments on two public datasets and compare the proposed method with state-of-the-art semantic segmentation methods. The experiments show that our method could achieve better building extraction results in HSR remote sensing image, which proves the effectiveness of our proposed method.

Forests are an essential part of the ecosystem and play an irreplaceable role in maintaining the balance of the ecosystem and protecting biodiversity. The monitoring of forest distribution plays an important role in the conservation and management of forests. This paper analyzes and compares the performance of imagery from GF-1 WFV, Landsat 8, and Sentinel-2 satellites with respect to forest/non-forest classification tasks using the random forest algorithm (RF). The results show that in the classification task of this paper, although the differences in classification accuracy among the three satellite datasets are not remarkable, the Sentinel-2 data have the highest accuracy, GF-1 WFV the second highest, and Landsat 8 the lowest. In addition, it was found that remotely sensed data of different processing levels show little influence on the classification accuracy with respect to the forest/non-forest classification task. However, the classification accuracy of the top of the atmosphere reflectance product was the most stable, and the vegetation index has a marginal effect on the distinction between forest and non-forest areas.