2016
DOI: 10.3390/rs8070555
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Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Abstract: Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this pa… Show more

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Cited by 27 publications
(17 citation statements)
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“…To remedy the limitations of handcrafted features, learning features automatically from images is considered as a more feasible strategy. In recent years, unsupervised feature learning from unlabeled input data has become an attractive alternative to handcrafted features and has made significant progress for remote sensing image scene classification [20,26,28,33,37,54,63,87,95,[126][127][128][129][130][131][132][133][134]. Unsupervised feature learning aims to learn a set of basis functions (or filters) used for feature encoding, in which the input of the functions is a set of handcrafted features or raw pixel intensity values and the output is a set of learned features.…”
Section: B Unsupervised Feature Learning Based Methodsmentioning
confidence: 99%
“…To remedy the limitations of handcrafted features, learning features automatically from images is considered as a more feasible strategy. In recent years, unsupervised feature learning from unlabeled input data has become an attractive alternative to handcrafted features and has made significant progress for remote sensing image scene classification [20,26,28,33,37,54,63,87,95,[126][127][128][129][130][131][132][133][134]. Unsupervised feature learning aims to learn a set of basis functions (or filters) used for feature encoding, in which the input of the functions is a set of handcrafted features or raw pixel intensity values and the output is a set of learned features.…”
Section: B Unsupervised Feature Learning Based Methodsmentioning
confidence: 99%
“…Interpretation of High-resolution Satellite Images 1) Scene Classification: Scene classification, which aims to automatically assign a semantic label to each scene image, has been an active research topic in the field of high-resolution satellite images in the past decades [68][69][70][71][72][73][74]. As a key problem in the interpretation of satellite images, it has widespread applications, including object detection [75,76], change detection [77], urban planning, land resource management, etc.…”
Section: B Interpretation Of Sar Imagesmentioning
confidence: 99%
“…Semantic-based scene classification has been widely applied in HRS image scene interpretation [15,16]. It is usually difficult to understand and recognize scene categories because of the high complexity of spatial and structural patterns in the massive HRS satellite images [17]. Therefore, feature representation in each scene is a key step and highly demanded for accurate scene classification.…”
Section: Introductionmentioning
confidence: 99%