Very high resolution remote sensing image classification with SEEDS-CNN and scale effect analysis for superpixel CNN classification

International Journal of Remote Sensing

Volumn 40, Issue 2, 2019, Pages 506-531

  Lv, Xianwei ^a   Ming, Dongping ^a   Chen, YangYang ^a   Wang, Min ^b  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b NANJING NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPLEX NETWORKS; DEEP LEARNING; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; NEURAL NETWORKS; PIXELS; REMOTE SENSING; SUPERPIXELS;

ARTIFICIAL OBJECTS; CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; CONVOLUTIONAL NEURAL NETWORKS (CNN); GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS; SUPERPIXEL SEGMENTATIONS; VERY HIGH RESOLUTION; VERY HIGH RESOLUTION (VHR) IMAGE;

DATA MINING;

ARTIFICIAL NEURAL NETWORK; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RESOLUTION; PERFORMANCE ASSESSMENT; PIXEL; REMOTE SENSING; SEGMENTATION;

EID: 85053519750     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2018.1513666     Document Type: Article

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