2018/11/19 by Pankaj Bodani, P. Bodani, Kumar Shreshtha +3 · 3 citations
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Convolutional neural network #Encoder #Encoding (memory) #Feature (linguistics) #Multispectral image #Orthophoto #Pattern recognition (psychology) #Remote-Sensing Image Classification #Segmentation #Softmax function #acm:68T45 #cs.CV #msc:68T45
paper · pdf · doi:10.5194/isprs-archives-xlii-5-621-2018
published in The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences XLII-5, 621-628 (Copernicus Publications) · 8 pages, 9 figures, 3 tables
openalex publication_date 2018/11/19 · arxiv created 2018/11/20 · arxiv updated 2018/11/21 · openalex created_date 2018/11/29 · openalex updated_date 2026/08/05
Abstract. This paper addresses the task of semantic segmentation of orthoimagery using multimodal data e.g. optical RGB, infrared and digital surface model. We propose a deep convolutional neural network architecture termed OrthoSeg for semantic segmentation using multimodal, orthorectified and coregistered data. We also propose a training procedure for supervised training of OrthoSeg. The training procedure complements the inherent architectural characteristics of OrthoSeg for preventing complex co-adaptations of learned features, which may arise due to probable high dimensionality and spatial correlation in multimodal and/or multispectral coregistered data. OrthoSeg consists of parallel encoding networks for independent encoding of multimodal feature maps and a decoder designed for efficiently fusing independently encoded multimodal feature maps. A softmax layer at the end of the network uses the features generated by the decoder for pixel-wise classification. The decoder fuses feature maps from the parallel encoders locally as well as contextually at multiple scales to generate per-pixel feature maps for final pixel-wise classification resulting in segmented output. We experimentally show the merits of OrthoSeg by demonstrating state-of-the-art accuracy on the ISPRS Potsdam 2D Semantic Segmentation dataset. Adaptability is one of the key motivations behind OrthoSeg so that it serves as a useful architectural option for a wide range of problems involving the task of semantic segmentation of coregistered multimodal and/or multispectral imagery. Hence, OrthoSeg is designed to enable independent scaling of parallel encoder networks and decoder network to better match application requirements, such as the number of input channels, the effective field-of-view, and model capacity.