Large-Scale Geospatial Data Analysis: Geographic Object-Based Scene Classification in Remote Sensing Images by GIS and Deep Residual Learning

The segmentation of remote sensing images with high spatial resolution is important and fundamental in geographic object-based image analysis (GEOBIA), so evaluating segmentation results without prior knowledge is an essential part in segmentation algorithms comparison, segmentation parameters selection, and optimization. In this study, we proposed a fast and effective unsupervised evaluation (UE) method using the area-weighted variance (WV) as intra-segment homogeneity and the difference to neighbor pixels (DTNP) as inter-segment heterogeneity. Then these two measures were combined into a fast-global score (FGS) to evaluate the segmentation. The effectiveness of DTNP and FGS was demonstrated by visual interpretation as qualitative analysis and supervised evaluation (SE) as quantitative analysis. For this experiment, the ‘‘Multi-resolution Segmentation’’ algorithm in eCognition was adopted in the segmentation and four typical study areas of GF-2 images were used as test data. The effectiveness analysis of DTNP shows that it can keep stability and remain sensitive to both over-segmentation and under-segmentation compared to two existing inter-segment heterogeneity measures. The effectiveness and computational cost analysis of FGS compared with two existing UE methods revealed that FGS can effectively evaluate segmentation results with the lowest computational cost.

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Evaluation of convnets for large-scale scene classification from high-resolution remote sensing images

IEEE EUROCON 2017 -17th International Conference on Smart Technologies ◽

10.1109/eurocon.2017.8011248 ◽

2017 ◽

Cited By ~ 2

Author(s):

Ratko Pilipovic ◽

Vladimir Risojevic

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Scene Classification ◽

Remote Sensing Images

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MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3454009 ◽

2021 ◽

Vol 17 (2s) ◽

pp. 1-11

Author(s):

Xiaochuan Tang ◽

Mingzhe Liu ◽

Hao Zhong ◽

Yuanzhen Ju ◽

Weile Li ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Large Scale ◽

Remote Sensing Image ◽

Disaster Risk ◽

Multiple Instance Learning ◽

Remote Sensing Images ◽

Loess Area ◽

Remote Sensing Image Classification ◽

Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

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Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2013.05.003 ◽

2013 ◽

Vol 82 ◽

pp. 125-137 ◽

Cited By ~ 107

Author(s):

Damien Arvor ◽

Laurent Durieux ◽

Samuel Andrés ◽

Marie-Angélique Laporte

Keyword(s):

Remote Sensing ◽

Image Analysis ◽

Object Based Image Analysis ◽

Object Based ◽

Geographic Object

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Climatic data analysis and computer data simulation of inland cities based on cloud computing and remote sensing images

Arabian Journal of Geosciences ◽

10.1007/s12517-021-07275-0 ◽

2021 ◽

Vol 14 (11) ◽

Author(s):

Qiao Zhou

Keyword(s):

Remote Sensing ◽

Cloud Computing ◽

Data Analysis ◽

Climatic Data ◽

Remote Sensing Images ◽

Data Simulation ◽

Computer Data

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A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Remote Sensing ◽

10.3390/rs13030433 ◽

2021 ◽

Vol 13 (3) ◽

pp. 433

Author(s):

Junge Shen ◽

Tong Zhang ◽

Yichen Wang ◽

Ruxin Wang ◽

Qi Wang ◽

...

Keyword(s):

Remote Sensing ◽

Feature Representation ◽

Dual Model ◽

Scene Classification ◽

Remote Sensing Images ◽

Single Model ◽

Fusion Strategy ◽

Multi Scale ◽

The Arts ◽

Scene Representation

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

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Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽

10.3390/s18103232 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3232 ◽

Cited By ~ 17

Author(s):

Yan Liu ◽

Qirui Ren ◽

Jiahui Geng ◽

Meng Ding ◽

Jiangyun Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Training Data ◽

Land Resources ◽

Remote Sensing Images ◽

Training Strategy ◽

The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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