Beyond pixels:Learning from multimodal hyperspectral superpixels for land cover classification  

作　　者：HONG DanFeng WU Xin YAO Jing ZHU XiaoXiang 

机构地区：[1]Key Laboratory of Computational Optical Imaging Technology,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China [2]School of Computer Science(National Pilot Software Engineering School),Beijing University of Posts and Telecommunications,Beijing 100876,China [3]Remote Sensing Technology Institute,German Aerospace Center(DLR),Wessling 82234,Germany [4]Data Science in Earth Observation,Technical University of Munich,Munich 80333,Germany

出　　处：《Science China(Technological Sciences)》2022年第4期802-808,共7页中国科学（技术科学英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant Nos. 62161160336, 42030111, and 62101045);the China Postdoctoral Science Foundation Funded Project (Grant No. 2021M690385)

摘　　要：Despite tons of advanced classification models that have recently been developed for the land cover mapping task,the monotonicity of a single remote sensing data source,such as only using hyperspectral data or multispectral data,hinders the classification accuracy from being further improved and tends to meet the performance bottleneck.For this reason,we develop a novel superpixel-based subspace learning model,called Supace,by jointly learning multimodal feature representations from HS and MS superpixels for more accurate LCC results.Supace can learn a common subspace across multimodal RS data,where the diverse and complementary information from different modalities can be better combined,being capable of enhancing the discriminative ability of to-be-learned features in a more effective way.To better capture semantic information of objects in the feature learning process,superpixels that beyond pixels are regarded as the study object in our Supace for LCC.Extensive experiments have been conducted on two popular hyperspectral and multispectral datasets,demonstrating the superiority of the proposed Supace in the land cover classification task compared with several well-known baselines related to multimodal remote sensing image feature learning.

关 键 词：CLASSIFICATION hyperspectral image land cover MULTIMODAL multispectral image remote sensing subspace learning superpixels 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]