کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
555014 1451261 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning multiscale and deep representations for classifying remotely sensed imagery
ترجمه فارسی عنوان
آموزش تضمینی چند مقیاسی و عمیق برای طبقه بندی تصاویر سنجش از راه دور
کلمات کلیدی
شبکه های عصبی کانولوشن چندمقیاسی (MCNN)؛ یادگیری عمیق؛ استخراج ویژگی؛ طبقه بندی تصویر سنجش از دور
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر سیستم های اطلاعاتی
چکیده انگلیسی

It is widely agreed that spatial features can be combined with spectral properties for improving interpretation performances on very-high-resolution (VHR) images in urban areas. However, many existing methods for extracting spatial features can only generate low-level features and consider limited scales, leading to unpleasant classification results. In this study, multiscale convolutional neural network (MCNN) algorithm was presented to learn spatial-related deep features for hyperspectral remote imagery classification. Unlike traditional methods for extracting spatial features, the MCNN first transforms the original data sets into a pyramid structure containing spatial information at multiple scales, and then automatically extracts high-level spatial features using multiscale training data sets. Specifically, the MCNN has two merits: (1) high-level spatial features can be effectively learned by using the hierarchical learning structure and (2) multiscale learning scheme can capture contextual information at different scales. To evaluate the effectiveness of the proposed approach, the MCNN was applied to classify the well-known hyperspectral data sets and compared with traditional methods. The experimental results shown a significant increase in classification accuracies especially for urban areas.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: ISPRS Journal of Photogrammetry and Remote Sensing - Volume 113, March 2016, Pages 155–165
نویسندگان
, ,