Land Cover Classification From VHR Optical Remote Sensing Images by Feature Ensemble Deep Learning Network

Dong, Shan; Zhuang, Yin; Yang, Zhanxin; Pang, Lei; Chen, He; Li, Teng

doi:10.1109/lgrs.2019.2947022

Cited by 30 publications

(17 citation statements)

References 14 publications

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“…Therewith, some results gain more attention than others. In Earth observation, the modules are often called channel and spatial attention modules: where channel attention modules weight the channels that hold the extracted features globally and spatial attention modules weight areas of those channels spatially [128,143,164,197,219,276,342,348,349]. This technique supports the idea that not all features which are extracted by a neural network affect the results equally.…”

Section: Image Segmentationmentioning

confidence: 92%

“…In Earth observation applications, image segmentation must deal with blobby results which are contrary to the intent to segment details and fine-grained class boundaries. In order to overcome this contradiction, atrous convolutions and the effective atrous spatial pyramid pooling module (ASPP) from the DeepLab family [333][334][335][336] were integrated into the U-Net in multiple studies [133,169,196,215,221,276,285,309,[337][338][339][340][341][342]. Atrous convolution maintains image resolution during feature extraction, which supports the attention to detail [151,163,202,343,344], where the ASPP module also takes spatial context into account which results in less blobby segmentation masks [145,146,345].…”

Section: Image Segmentationmentioning

confidence: 99%

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Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020

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In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

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Section: Image Segmentationmentioning

confidence: 92%

Section: Image Segmentationmentioning

confidence: 99%

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020

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“…FCNs have been widely used for heterogeneous remote sensing images joint land cover classification but do not include optical and SAR images. However, there have been many FCNbased land cover classification studies for single-source remote sensing images, either optical [23]- [25] or SAR [26], [27]. This is because it is barely usable optical and SAR image datasets to train an FCN.…”

Section: Introductionmentioning

confidence: 99%

CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images

Kang

Xiang

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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As two of the most widely-used remote sensing images, optical and synthetic aperture radar (SAR) images show abundant and complementary information on the same target owing to their individual imaging mechanisms. Consequently, using optical and SAR images simultaneously can better describe the inherent features of the target and thus be beneficial for subsequent remote sensing applications. In this paper, we propose a novel modular fully convolutional network model to improve the accuracy of land cover classification by fully exploiting the complementary features of the two sensors. We investigate where and how to fuse the two images in the joint classification network. A cross-gate module with bidirectional information flow is proposed to achieve the best fusion performance. In addition, to validate the proposed model, we construct a multiclass land cover classification dataset. Exhaustive experiments show that the proposed joint classification network presents superior results than state-of-the-art classification models using single-sensor images.

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“…Aiming at the problem that traditional remote sensing image classification methods are vulnerable to the loss of spatial features, reference [ 17 ] proposed an image semantic segmentation method based on a dense coordinate transformation network, which improves the accuracy of semantic segmentation of high-resolution remote sensing images but still has a certain dependence on the training data set. Reference [ 18 ] proposed a feature integration network including multiscale features and enhancement stages for the classification of land remote sensing images and used two-dimensional extended convolution with different sampling rates for each scale feature layer to realize image classification with higher accuracy than ordinary depth learning methods, but the classification efficiency needs to be further improved.…”

Section: Introductionmentioning

confidence: 99%

Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images

Xia

Kong

Jun

et al. 2022

Computational Intelligence and Neuroscience

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Aiming at the problems that the traditional remote sensing image classification methods cannot effectively integrate a variety of deep learning features and poor classification performance, a land resource use classification method based on a convolutional neural network (CNN) in ecological remote sensing images is proposed. In this study, a seven-layer convolution neural network is constructed, and then the two fully connected layer features of the improved CNN network training output are fused with the fifth layer pooled layer features after dimensionality reduction by principal component analysis (PCA), so as to obtain an effective remote sensing image feature of land resources based on deep learning. Further, the classification of land resources remote sensing images is completed based on a support vector machine classifier. The remote sensing images of Pingshuo mining area in Shanxi Province are used to analyze the proposed method. The results show that the edge of the recognized image is clear, the classification accuracy, misclassification rate, and kappa coefficient are 0.9472, 0.0528, and 0.9435, respectively, and the model has excellent overall performance and good classification effect.

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Land Cover Classification From VHR Optical Remote Sensing Images by Feature Ensemble Deep Learning Network

Cited by 30 publications

References 14 publications

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

CFNet: A Cross Fusion Network for Joint Land Cover Classification Using Optical and SAR Images

Land Resource Use Classification Using Deep Learning in Ecological Remote Sensing Images

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