Semantic Labeling of Aerial and Satellite Imagery

Paisitkriangkrai, Sakrapee; Sherrah, Jamie; Janney, Pranam; Hengel, Anton van den

doi:10.1109/jstars.2016.2582921

Cited by 119 publications

(80 citation statements)

References 38 publications

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“…Recent advances in image-based classification that were also adapted for land cover classification (Paisitkriangkrai et al, 2016;Marmanis et al, 2018) relied on CNN, see also the recent overview of (Zhu et al, 2017). This resulted in a considerable improvement in the classification accuracy that can be achieved, which is usually attributed to the fact that using CNN, high-level features can be learned from training data.…”

Section: Related Workmentioning

confidence: 99%

“…This task is challenging due to the heterogeneous appearance and high intra-class variance of objects, e.g. (Paisitkriangkrai et al, 2016). In contrast, land use describes the socio-economic function of a piece of land (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Recent work on the classification of images has focused on convolutional neural networks (CNN). Originally developed for predicting one class label per image (Krizhevsky et al, 2012), they have been expanded to pixel-based classification of images (semantic segmentation) (Badrinarayanan et al, 2017) and also to classification of land cover based on aerial images (Audebert et al, 2016;Paisitkriangkrai et al, 2016). CNN have outperformed other classifiers for pixel-based classification by a large margin if a sufficient amount of training data is available.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

Chun

Rottensteiner

Heipke

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7% and 77.4% can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Land Cover and Land Use Based on Convolutional Neural Networks

Chun

Rottensteiner

Heipke

2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…They deliver stateof-the-art performance on the ISPRS semantic labeling dataset. With the same types of data, Paisitkriangkrai et al (2016) used both hand-crafted features from (Gerke, 2014) and CNN features to produce their final prediction. CNN features are actually outputs of the convolutional part of three different CNNs, each with a different image size as input to capture a large context, but preserving high-frequency information.…”

Section: Scene Parsing In Overhead Imagerymentioning

confidence: 99%

“…Such imagery can be captured yearly at the country scale and may be used to monitor changes. In very recent years, various works have already shown the efficiency of deep architectures for semantic segmentation of geospatial VHR images (see for instance (Marmanis et al, 2016a,b;Paisitkriangkrai et al, 2016;Maggiori et al, 2017;Volpi and Tuia, 2017). However, they remain at an experimental level, they also focus on sharp class boundary detection and require data that may not be available/updated yearly at large scales (i.e., Digital Surface Models and/or sub-meter spatial resolution images).…”

Section: Introductionmentioning

confidence: 99%

Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

Postadjian¹,

Bris²,

Sahbi³

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training.

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Bibliography

2021

Deep Learning for the Earth Sciences

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Semantic Labeling of Aerial and Satellite Imagery

Cited by 119 publications

References 38 publications

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

Investigating the Potential of Deep Neural Networks for Large-Scale Classification of Very High Resolution Satellite Images

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