187 research outputs found
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Fuzzy Superpixels based Semi-supervised Similarity-constrained CNN for PolSAR Image Classification
Recently, deep learning has been highly successful in image classification. Labeling the PolSAR data, however, is time-consuming and laborious and in response semi-supervised deep learning has been increasingly investigated in PolSAR image classification. Semi-supervised deep learning methods for PolSAR image classification can be broadly divided into two categories, namely pixels-based methods and superpixels-based methods. Pixels-based semi-supervised methods are liable to be affected by speckle noises and have a relatively high computational complexity. Superpixels-based methods focus on the superpixels and ignore tiny detail-preserving represented by pixels. In this paper, a Fuzzy superpixels based Semi-supervised Similarity-constrained CNN (FS-SCNN) is proposed. To reduce the effect of speckle noises and preserve the details, FS-SCNN uses a fuzzy superpixels algorithm to segment an image into two parts, superpixels and undetermined pixels. Moreover, the fuzzy superpixels algorithm can also reduce the number of mixed superpixels and improve classification performance. To exploit unlabeled data effectively, we also propose a Similarity-constrained Convolutional Neural Network (SCNN) model to assign pseudo labels to unlabeled data. The final training set consists of the initial labeled data and these pseudo labeled data. Three PolSAR images are used to demonstrate the excellent classification performance of the FS-SCNN method with data of limited labels
Multi-frequency PolSAR Image Fusion Classification Based on Semantic Interactive Information and Topological Structure
Compared with the rapid development of single-frequency multi-polarization
SAR image classification technology, there is less research on the land cover
classification of multifrequency polarimetric SAR (MF-PolSAR) images. In
addition, the current deep learning methods for MF-PolSAR classification are
mainly based on convolutional neural networks (CNNs), only local spatiality is
considered but the nonlocal relationship is ignored. Therefore, based on
semantic interaction and nonlocal topological structure, this paper proposes
the MF semantics and topology fusion network (MF-STFnet) to improve MF-PolSAR
classification performance. In MF-STFnet, two kinds of classification are
implemented for each band, semantic information-based (SIC) and topological
property-based (TPC). They work collaboratively during MF-STFnet training,
which can not only fully leverage the complementarity of bands, but also
combine local and nonlocal spatial information to improve the discrimination
between different categories. For SIC, the designed crossband interactive
feature extraction module (CIFEM) is embedded to explicitly model the deep
semantic correlation among bands, thereby leveraging the complementarity of
bands to make ground objects more separable. For TPC, the graph sample and
aggregate network (GraphSAGE) is employed to dynamically capture the
representation of nonlocal topological relations between land cover categories.
In this way, the robustness of classification can be further improved by
combining nonlocal spatial information. Finally, an adaptive weighting fusion
(AWF) strategy is proposed to merge inference from different bands, so as to
make the MF joint classification decisions of SIC and TPC. The comparative
experiments show that MF-STFnet can achieve more competitive classification
performance than some state-of-the-art methods
Incorporating interferometric coherence into lulc classification of airborne polsar-images using fully convolutional networks
Inspired by the application of state-of-the-art Fully Convolutional Networks (FCNs) for the semantic segmentation of high-resolution optical imagery, recent works transfer this methodology successfully to pixel-wise land use and land cover (LULC) classification of PolSAR data. So far, mainly single PolSAR images are included in the FCN-based classification processes. To further increase classification accuracy, this paper presents an approach for integrating interferometric coherence derived from co-registered image pairs into a FCN-based classification framework. A network based on an encoder-decoder structure with two separated encoder branches is presented for this task. It extracts features from polarimetric backscattering intensities on the one hand and interferometric coherence on the other hand. Based on a joint representation of the complementary features pixel-wise classification is performed. To overcome the scarcity of labelled SAR data for training and testing, annotations are generated automatically by fusing available LULC products. Experimental evaluation is performed on high-resolution airborne SAR data, captured over the German Wadden Sea. The results demonstrate that the proposed model produces smooth and accurate classification maps. A comparison with a single-branch FCN model indicates that the appropriate integration of interferometric coherence enables the improvement of classification performance
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