Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network

Zhang, Xiaodong; Zhu, Kun; Chen, Guanzhou; Tan, Xiaoliang; Zhang, Lifei; Fan, Deng-Ping; Liao, Puyun; Gong, Yue

doi:10.3390/rs11070755

Cited by 74 publications

(48 citation statements)

References 54 publications

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“…To deal with the problem of multi-scale detection with the large ratio of remote sensing objects, Guo et al [26] proposed a unified multi-scale framework, which is composed of multi-scale object proposal network and a multi-scale detection network. To achieve further accuracy of the localization in aerial images, Zhang et al [27] proposed a Double Multi-scale Feature Pyramid Network (DM-FPN), which makes the most of semantic and resolution features simultaneously and bring up some multi-scale training, inference and adaptive categorical non-maximum suppression (ACNMS) strategies. In addition, object detection based on weakly supervised deep learning method arouses more and more attentions of researchers in recent years.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images

Yang

et al. 2020

Sensors

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Accurate and robust detection of multi-class objects in very high resolution (VHR) aerial images has been playing a significant role in many real-world applications. The traditional detection methods have made remarkable progresses with horizontal bounding boxes (HBBs) due to CNNs. However, HBB detection methods still exhibit limitations including the missed detection and the redundant detection regions, especially for densely-distributed and strip-like objects. Besides, large scale variations and diverse background also bring in many challenges. Aiming to address these problems, an effective region-based object detection framework named Multi-scale Feature Integration Attention Rotation Network (MFIAR-Net) is proposed for aerial images with oriented bounding boxes (OBBs), which promotes the integration of the inherent multi-scale pyramid features to generate a discriminative feature map. Meanwhile, the double-path feature attention network supervised by the mask information of ground truth is introduced to guide the network to focus on object regions and suppress the irrelevant noise. To boost the rotation regression and classification performance, we present a robust Rotation Detection Network, which can generate efficient OBB representation. Extensive experiments and comprehensive evaluations on two publicly available datasets demonstrate the effectiveness of the proposed framework.

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Section: Introductionmentioning

confidence: 99%

Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images

Yang

et al. 2020

Sensors

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“…[64] Used a classical Fuzzy C-means (FCM) method was for the coastline detection, but had been improved by combining the Wavelet decomposition algorithm to better suppress the inherent speckle noises of SAR image. In [65], [66] the authors proposed an end-to-end framework called multiple feature pyramid network (MFPN). In MFPN, an effective feature pyramid and a tailored pyramid pooling module are implemented that takes advantage of multilevel semantic features of high resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali

Zareapoor

Wang

et al. 2019

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

103

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Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sealand segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixelwise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each down-and up-sampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multi-scale contextual information. Each dense network block contains multilevel convolution layers, short-range connections and an identity mapping connection which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results whilst minimizing computational costs. We have performed extensive experiments on two real datasets Google-Earth and ISPRS and compare the proposed RDU-Net against several variations of Dense Networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks.Index Term-Deep neural network (DNN), dense network (DenseNet), remote sensing images, sea-land segmentation, U-Net.

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“…On this basis, object detection in remote sensing imagery has been widely studied in recent years [27][28][29][30][31][32]. In the field of remote sensing, many researchers have made great efforts to object detection methods based on CNN [33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

“…Note that detectors in this model are used in conjunction with multi-scale feature fusion modules. Based on the SSD [25], a novel single-shot detector named the Recurrent Detection with Activated Semantics (RDAS) structure is presented for addressing the small-scaled object fast detection problem in VHR remote sensing in [35]. Besides, the shared multi-scale base network and the multi-scale object proposal network were employed in [38], which enables the production of feature maps with high semantic information at different layers and generation of anchor boxes that cover most of the objects with a small number of negative samples.…”

Section: Introductionmentioning

confidence: 99%

“…For the experiments, we adopted the ACS dataset consisting of 3 categorieswhich is a collection of five public datasetsand the NWPU VHR-10 dataset [41,45,58,59] which is a 10-class HSR remote sensing imagery geospatial object detection dataset. Note that the methods proposed in [33,35,41,58,59] are all evaluated in the NWPU VHR-10 dataset. The experimental results confirmed that the proposed method can achieve a satisfactory detection performance in remote sensing object detection.…”

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confidence: 99%

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A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

Xie

Qin

et al. 2019

Remote Sensing

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With great significance in military and civilian applications, the topic of detecting small and densely arranged objects in wide-scale remote sensing imagery is still challenging nowadays. To solve this problem, we propose a novel effectively optimized one-stage network (NEOON). As a fully convolutional network, NEOON consists of four parts: Feature extraction, feature fusion, feature enhancement, and multi-scale detection. To extract effective features, the first part has implemented bottom-up and top-down coherent processing by taking successive down-sampling and up-sampling operations in conjunction with residual modules. The second part consolidates high-level and low-level features by adopting concatenation operations with subsequent convolutional operations to explicitly yield strong feature representation and semantic information. The third part is implemented by constructing a receptive field enhancement (RFE) module and incorporating it into the fore part of the network where the information of small objects exists. The final part is achieved by four detectors with different sensitivities accessing the fused features, all four parallel, to enable the network to make full use of information of objects in different scales. Besides, the Focal Loss is set to enable the cross entropy for classification to solve the tough problem of class imbalance in one-stage methods. In addition, we introduce the Soft-NMS to preserve accurate bounding boxes in the post-processing stage especially for densely arranged objects. Note that the split and merge strategy and multi-scale training strategy are employed in training. Thorough experiments are performed on ACS datasets constructed by us and NWPU VHR-10 datasets to evaluate the performance of NEOON. Specifically, 4.77% and 5.50% improvements in mAP and recall, respectively, on the ACS dataset as compared to YOLOv3 powerfully prove that NEOON can effectually improve the detection accuracy of small objects in remote sensing imagery. In addition, extensive experiments and comprehensive evaluations on the NWPU VHR-10 dataset with 10 classes have illustrated the superiority of NEOON in the extraction of spatial information of high-resolution remote sensing images.

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Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network

Cited by 74 publications

References 54 publications

Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images

Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

A Novel Effectively Optimized One-Stage Network for Object Detection in Remote Sensing Imagery

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