11 research outputs found
SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion
The availability of curated large-scale training data is a crucial factor for
the development of well-generalizing deep learning methods for the extraction
of geoinformation from multi-sensor remote sensing imagery. While quite some
datasets have already been published by the community, most of them suffer from
rather strong limitations, e.g. regarding spatial coverage, diversity or simply
number of available samples. Exploiting the freely available data acquired by
the Sentinel satellites of the Copernicus program implemented by the European
Space Agency, as well as the cloud computing facilities of Google Earth Engine,
we provide a dataset consisting of 180,662 triplets of dual-pol synthetic
aperture radar (SAR) image patches, multi-spectral Sentinel-2 image patches,
and MODIS land cover maps. With all patches being fully georeferenced at a 10 m
ground sampling distance and covering all inhabited continents during all
meteorological seasons, we expect the dataset to support the community in
developing sophisticated deep learning-based approaches for common tasks such
as scene classification or semantic segmentation for land cover mapping.Comment: accepted for publication in the ISPRS Annals of the Photogrammetry,
Remote Sensing and Spatial Information Sciences (online from September 2019
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Designing discriminative powerful texture features robust to realistic
imaging conditions is a challenging computer vision problem with many
applications, including material recognition and analysis of satellite or
aerial imagery. In the past, most texture description approaches were based on
dense orderless statistical distribution of local features. However, most
recent approaches to texture recognition and remote sensing scene
classification are based on Convolutional Neural Networks (CNNs). The d facto
practice when learning these CNN models is to use RGB patches as input with
training performed on large amounts of labeled data (ImageNet). In this paper,
we show that Binary Patterns encoded CNN models, codenamed TEX-Nets, trained
using mapped coded images with explicit texture information provide
complementary information to the standard RGB deep models. Additionally, two
deep architectures, namely early and late fusion, are investigated to combine
the texture and color information. To the best of our knowledge, we are the
first to investigate Binary Patterns encoded CNNs and different deep network
fusion architectures for texture recognition and remote sensing scene
classification. We perform comprehensive experiments on four texture
recognition datasets and four remote sensing scene classification benchmarks:
UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with
7 categories and the recently introduced large scale aerial image dataset (AID)
with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary
information to standard RGB deep model of the same network architecture. Our
late fusion TEX-Net architecture always improves the overall performance
compared to the standard RGB network on both recognition problems. Our final
combination outperforms the state-of-the-art without employing fine-tuning or
ensemble of RGB network architectures.Comment: To appear in ISPRS Journal of Photogrammetry and Remote Sensin
AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints
Real-time and accurate information on fine-grained changes in crop
cultivation is of great significance for crop growth monitoring, yield
prediction and agricultural structure adjustment. Aiming at the problems of
serious spectral confusion in visible high-resolution unmanned aerial vehicle
(UAV) images of different phases, interference of large complex background and
salt-and-pepper noise by existing semantic change detection (SCD) algorithms,
in order to effectively extract deep image features of crops and meet the
demand of agricultural practical engineering applications, this paper designs
and proposes an agricultural geographic scene and parcel-scale constrained SCD
framework for crops (AGSPNet). AGSPNet framework contains three parts:
agricultural geographic scene (AGS) division module, parcel edge extraction
module and crop SCD module. Meanwhile, we produce and introduce an UAV image
SCD dataset (CSCD) dedicated to agricultural monitoring, encompassing multiple
semantic variation types of crops in complex geographical scene. We conduct
comparative experiments and accuracy evaluations in two test areas of this
dataset, and the results show that the crop SCD results of AGSPNet consistently
outperform other deep learning SCD models in terms of quantity and quality,
with the evaluation metrics F1-score, kappa, OA, and mIoU obtaining
improvements of 0.038, 0.021, 0.011 and 0.062, respectively, on average over
the sub-optimal method. The method proposed in this paper can clearly detect
the fine-grained change information of crop types in complex scenes, which can
provide scientific and technical support for smart agriculture monitoring and
management, food policy formulation and food security assurance
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
Hyperspectral Image Classification -- Traditional to Deep Models: A Survey for Future Prospects
Hyperspectral Imaging (HSI) has been extensively utilized in many real-life
applications because it benefits from the detailed spectral information
contained in each pixel. Notably, the complex characteristics i.e., the
nonlinear relation among the captured spectral information and the
corresponding object of HSI data make accurate classification challenging for
traditional methods. In the last few years, Deep Learning (DL) has been
substantiated as a powerful feature extractor that effectively addresses the
nonlinear problems that appeared in a number of computer vision tasks. This
prompts the deployment of DL for HSI classification (HSIC) which revealed good
performance. This survey enlists a systematic overview of DL for HSIC and
compared state-of-the-art strategies of the said topic. Primarily, we will
encapsulate the main challenges of traditional machine learning for HSIC and
then we will acquaint the superiority of DL to address these problems. This
survey breakdown the state-of-the-art DL frameworks into spectral-features,
spatial-features, and together spatial-spectral features to systematically
analyze the achievements (future research directions as well) of these
frameworks for HSIC. Moreover, we will consider the fact that DL requires a
large number of labeled training examples whereas acquiring such a number for
HSIC is challenging in terms of time and cost. Therefore, this survey discusses
some strategies to improve the generalization performance of DL strategies
which can provide some future guidelines