TY - GEN
T1 - Npaloss: Neighboring Pixel Affinity Loss for Semantic Segmentation in High-Resolution Aerial Imagery
AU - Feng, Yingchao
AU - Diao, Wenhui
AU - Sun, Xian
AU - Li, Jihao
AU - Chen, K.
AU - Fu, K.
AU - Gao, X.
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-21
PY - 2020/8/3
Y1 - 2020/8/3
N2 - The performance of semantic segmentation in high-resolution aerial imagery has been improved rapidly through the introduction of deep fully convolutional neural network (FCN). However, due to the complexity of object shapes and sizes, the labeling accuracy of small-sized objects and object boundaries still need to be improved. In this paper, we propose a neighboring pixel affinity loss (NPALoss) to improve the segmentation performance of these hard pixels. Specifically, we address the issues of how to determine the classifying difficulty of one pixel and how to get the suitable weight margin between well-classified pixels and hard pixels. Firstly, we convert the first problem into a problem that the pixel categories in the neighborhood are the same or different. Based on this idea, we build a neighboring pixel affinity map by counting the pixel-pair relationships for each pixel in the search region. Secondly, we investigate different weight transformation strategies for the affinity map to explore the suitable weight margin and avoid gradient overflow. The logarithm compression strategy is better than the normalization strategy, especially the common logarithm. Finally, combining the affinity map and logarithm compression strategy, we build NPALoss to adaptively assign different weights for each pixel. Comparative experiments are conducted on the ISPRS Vaihingen dataset and several commonly-used state-of-the-art networks. We demonstrate that our proposed approach can achieve promising results.
AB - The performance of semantic segmentation in high-resolution aerial imagery has been improved rapidly through the introduction of deep fully convolutional neural network (FCN). However, due to the complexity of object shapes and sizes, the labeling accuracy of small-sized objects and object boundaries still need to be improved. In this paper, we propose a neighboring pixel affinity loss (NPALoss) to improve the segmentation performance of these hard pixels. Specifically, we address the issues of how to determine the classifying difficulty of one pixel and how to get the suitable weight margin between well-classified pixels and hard pixels. Firstly, we convert the first problem into a problem that the pixel categories in the neighborhood are the same or different. Based on this idea, we build a neighboring pixel affinity map by counting the pixel-pair relationships for each pixel in the search region. Secondly, we investigate different weight transformation strategies for the affinity map to explore the suitable weight margin and avoid gradient overflow. The logarithm compression strategy is better than the normalization strategy, especially the common logarithm. Finally, combining the affinity map and logarithm compression strategy, we build NPALoss to adaptively assign different weights for each pixel. Comparative experiments are conducted on the ISPRS Vaihingen dataset and several commonly-used state-of-the-art networks. We demonstrate that our proposed approach can achieve promising results.
UR - https://isprs-annals.copernicus.org/articles/V-2-2020/475/2020/
UR - http://www.scopus.com/inward/record.url?scp=85091114690&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-V-2-2020-475-2020
DO - 10.5194/isprs-annals-V-2-2020-475-2020
M3 - Conference contribution
SP - 475
EP - 482
BT - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PB - Copernicus [email protected]
ER -