人工智能
模式识别(心理学)
特征(语言学)
计算机科学
土地覆盖
像素
特征提取
分类器(UML)
遥感
加权
上下文图像分类
图像分辨率
光谱带
计算机视觉
地理
图像(数学)
土地利用
医学
哲学
语言学
放射科
土木工程
工程类
作者
Zhiyong Lv,Pengfei Zhang,Weiwei Sun,Jón Atli Benediktsson,Junhuai Li,Wei Wang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:61: 1-12
被引量:20
标识
DOI:10.1109/tgrs.2023.3275753
摘要
Spectral-spatial features are important for ground target identification and classification with High Spatial Resolution Remotely Sensed (HSRRS) Imagery. In this paper, two novel features, named the Gaussian-Weighting Spectral (GWS) feature and the Area Shape Index (ASI) feature, are proposed to complement the deficiency of the basic image feature for land cover classification with HSRRS imagery. The proposed GWS feature is an adaptive region-based feature that aims to improve the spectral homogeneity of a local area surrounding a pixel. Additionally, it is well known that the spectral feature is inadequate for classifying HSRRS imagery. Therefore, one spatial feature called the ASI feature is proposed here to describe the relationship between the area and shape for an adaptive region around each pixel. The proposed GWS and ASI features coupled with the basic red-green-blue feature are fed into a supervised classifier to obtain the final classification map. Experiments based on four real HSRRS images demonstrate that the proposed GWS and ASI features are capable of improving classification accuracies compared with some cognate state of the art methods. Moreover, the experiments also reveal that the proposed spectral-spatial features can complement each other for enhancing the classification performance with HSRRS images.
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