Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks
نویسندگان
چکیده
منابع مشابه
Land Cover Classification from Multi-temporal, Multi-spectral Remotely Sensed Imagery using Patch-Based Recurrent Neural Networks
Sustainability of the global environment is dependent on the accurate land cover information over large areas. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets were derived from a pixel-based singledate multi-spectral rem...
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Remote Sensing (RS) data and techniques, in combination with GIS and landscape metrics, are fundamental to analyse and characterise Land Cover (LC) and its changes. The case study here described, has been conducted in the area of Avellino (Southern Italy). To characterise the dynamics of changes during a fifty year period (1954÷2004), a multitemporal set of images has been processed: aerial pho...
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The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sens...
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Abstract: Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal observations. The increasing temporal capabilities of today’s sensors enable the use of temporal, along with spectral and spatial features. Domains such as speech recognition or neural machine tr...
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The importance of accurately describing the nature of land cover resources is increasing. With the aim to analyze the consistency of remotely sensed images from different sensors for land cover classification, three medium spatial resolution optical image sources in Xuzhou city were classified in the study, including CBERS, ETM, and ASTER. Land cover classification was conducted by Maximum Like...
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ژورنال
عنوان ژورنال: Neural Networks
سال: 2018
ISSN: 0893-6080
DOI: 10.1016/j.neunet.2018.05.019