A patch-based convolutional neural network for remote sensing image classification

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

doi:10.1016/j.neunet.2017.07.017

Cited by 219 publications

(132 citation statements)

References 27 publications

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“…CNNs learn in a supervised way, a hierarchy of filters to extract high-level features, using both spectral and spatial information. They have been used to perform classification in a patch-based way [11,12,13] and also to classify all the pixels of the input image in one forward pass [14,15].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Srivastava

Muñoz

Tuia

2019

Remote Sensing of Environment

121

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This is the pre-acceptance version, to read the final version published in the journal Remote Sensing of Environment, please go to: https:// doi.org/10.1016/j.rse.2019.04.014 Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive.Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities).We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area ofÎle-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France.Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it * First Author: SS, shivangi.srivastava@wur.nl.

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Section: Introduction and Related Workmentioning

confidence: 99%

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Srivastava

Muñoz

Tuia

2019

Remote Sensing of Environment

121

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“…In these classification methods, each scene image is resized to a rectangular patch for labeling [43][44][45]. In previous studies, researchers have used sliding window [46] or chessboard segmentation [47] to create rectangular patches. Based on these patches, representative features are calculated for classification.…”

Section: Related Methods and Advantagesmentioning

confidence: 99%

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Deng

et al. 2019

Sustainability

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Coastal aquaculture plays an important role in the provision of seafood, the sustainable development of regional and global economy, and the protection of coastal ecosystems. Inappropriate planning of disordered and intensive coastal aquaculture may cause serious environmental problems and socioeconomic losses. Precise delineation and classification of different kinds of aquaculture areas are vital for coastal management. It is difficult to extract coastal aquaculture areas using the conventional spectrum, shape, or texture information. Here, we proposed an object-based method combining multi-scale segmentation and object-based neighbor features to delineate existing coastal aquaculture areas. We adopted the multi-scale segmentation to generate semantically meaningful image objects for different land cover classes, and then utilized the object-based neighbor features for classification. Our results show that the proposed approach effectively identified different types of coastal aquaculture areas, with 96% overall accuracy. It also performed much better than other conventional methods (e.g., single-scale based classification with conventional features) with higher classification accuracy. Our results also suggest that the multi-scale segmentation and neighbor features can obviously improve the classification performance for the extraction of cage culture areas and raft culture areas, respectively. Our developed approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.Environmental Protection Law, overall marine functional zonation, and nature reserve schemes, have been formulated for the management of coastal areas. However, comprehensive coastal management in China is still a big challenge. Thus, the monitoring and management of coastal aquaculture are imperative to ensure sustainable development of marine aquaculture industry.Remote sensing technology has substantially improved our ability to observe remote or inaccessible areas at a fraction of the cost of traditional surveys [9]. Mapping and monitoring of aquaculture facilities provide decision-makers with important baseline data on production, cultivated area boundaries, and environmental impacts [10,11]. Remote sensing can provide consistent and wide-range monitoring using various sensors to support aquaculture management [12][13][14], which means the mapping of aquaculture facilities can be performed accurately and periodically at selected scales.Previous studies have used visual interpretation, spatial structure enhanced analysis, and object based image analysis (OBIA) to extract coastal aquaculture areas from remotely sensed imagery. Although visual interpretation can achieve the highest accuracy, it takes a lot of time and effort. Thus, it is less used presently. Spatial structure enhancement techniques, such as neighborhood and texture analysis, are the most commonly adopted methods in the classification process [15,16]. OBIA has become a widely used method in the past decades, especially for the classification o...

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“…Classification of these high-resolution images is similar to object recognition in computer vision, and remarkable improvements achieved by deep networks in object recognition have also been shown in these applications (Sharma et al, 2017). Due to their remarkable performance, these methods are used to analyze HRRS images, and have achieved more impressive results than the traditional shallow methods for scene classification (Castelluccio et al;Hu et al, 2015;Zhang et al, 2016;Zhao and Du, 2016;Luo et al, 2017;Wang et al, 2017;Cheng et al, 2016).…”

Section: Introductionmentioning

confidence: 96%

Application of Deep Learning of Multi-Temporal Sentinel-1 Images for the Classification of Coastal Vegetation Zone of the Danube Delta

Niculescu

Ienco²,

Hanganu

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Land cover is a fundamental variable for regional planning, as well as for the study and understanding of the environment. This work propose a multi-temporal approach relying on a fusion of radar multi-sensor data and information collected by the latest sensor (Sentinel-1) with a view to obtaining better results than traditional image processing techniques. The Danube Delta is the site for this work. The spatial approach relies on new spatial analysis technologies and methodologies: Deep Learning of multi-temporal Sentinel-1. We propose a deep learning network for image classification which exploits the multi-temporal characteristic of Sentinel-1 data. The model we employ is a Gated Recurrent Unit (GRU) Network, a recurrent neural network that explicitly takes into account the time dimension via a gated mechanism to perform the final prediction. The main quality of the GRU network is its ability to consider only the important part of the information coming from the temporal data discarding the irrelevant information via a forgetting mechanism. We propose to use such network structure to classify a series of images Sentinel-1 (20 Sentinel-1 images acquired between 9.10.2014 and 01.04.2016). The results are compared with results of the classification of Random Forest.

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A patch-based convolutional neural network for remote sensing image classification

Cited by 219 publications

References 27 publications

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Coastal Aquaculture Mapping from Very High Spatial Resolution Imagery by Combining Object-Based Neighbor Features

Application of Deep Learning of Multi-Temporal Sentinel-1 Images for the Classification of Coastal Vegetation Zone of the Danube Delta

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