A Convolutional Neural Network Model with Weighted Combination of Multi-scale Spatial Features for Crop Classification
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This article is published in Journal of remote sensing.The article was published on 2019-01-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Scale (ratio) & Convolutional neural network.read more
Citations
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Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery
TL;DR: Overshooting top (OT) play a crucial role in carrying tropospheric water vapor to the lower stratosphere and are closely related to climate change as well as local severe weather conditions as mentioned in this paper.
References
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Journal ArticleDOI
Deep Convolutional Neural Networks for Hyperspectral Image Classification
TL;DR: Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.
Journal ArticleDOI
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
TL;DR: This paper proposes two scenarios for generating image features via extracting CNN features from different layers and reveals that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features.
Journal ArticleDOI
Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains
TL;DR: In this article, the authors evaluated the applicability of time-series MODIS 250m normalized difference vegetation index (NDVI) data for large-area crop-related land use/land cover (LULC) mapping over the U.S. Central Great Plains.
Journal ArticleDOI
A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification
TL;DR: The proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination, paving the way to effectively address the complicated problem of VFSR image classification.
Proceedings ArticleDOI
Building detection in very high resolution multispectral data with deep learning features
TL;DR: An automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks based on a supervised classification procedure employing a very large training dataset is proposed.
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