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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Prabakar, D. Pranavan, S. Kumbhkar, Makhan Sah, Swati Patil, Harshal Firos, A. Computer Science and Engineering Karpagam College of Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Civil Engineering Navakkarai Coimbatore India Indian Institute of Soybean Research Department of Computer Application Indore India School of Set Sharda University Greater Noida India Pune India Rono-Hills Department of Computer Science and Engineering Arunachal Pradesh Doimukh India

ISBN: (纸本)9798350394580

By recording a wide range of spectral bands across the electromagnetic spectrum, hyperspectral imagery (HSI) delivers extensive information. Due to the complicated interaction between spectral and spatial properties, extracting meaningful geographical information from HSI is difficult. This article uses Spectral-Spatial Networks to detect geographical objects in hyperspectral data. The proposed deep learning system blends spectral and spatial information to improve object discrimination in complicated scenarios. SSN uses convolutional neural networks (CNNs) and spectral attention mechanisms to adaptively weigh spectral bands and capture spatial correlations. A large dataset of annotated hyperspectral photography with ground truth object identification trains the SSN. The collection includes different lighting, topography, and item scales. The object detection task is fine-tuned after pre-training on a large HSI dataset. Experimental results on benchmark hyperspectral datasets show that the SSN outperforms state-of-the-art approaches. Precision, recall, and F1-score for numerous object classes reveal that the suggested technique can distinguish objects in complicated and congested situations. The suggested SSN framework is also resilient to illumination, climatic conditions, and sensor properties, making it suitable for real-world applications. The SSN architecture's scalability is further examined, showing its suitability for heterogeneous HSI datasets with different spectral resolutions. © 2023 IEEE.

关键词： Environmental Monitoring geospatial object detection Hyperspectral Imagery Remote Sensing Spectral-Spatial Networks

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SINGLE-SHOT BALANCED DETECTOR FOR geospatial object detection 47

SINGLE-SHOT BALANCED DETECTOR FOR GEOSPATIAL OBJECT DETECTIO...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Liu, Yanfeng Li, Qiang Yuan, Yuan Wang, Qi Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China

ISBN: (纸本)9781665405409

geospatial object detection is an essential task in remote sensing community. One-stage methods based on deep learning have faster running speed but cannot reach higher detection accuracy than two-stage methods. In this paper, to achieve excellent speed/accuracy trade-off for geospatial object detection, a single-shot balanced detector is presented. First, a balanced feature pyramid network (BFPN) is designed, which can balance semantic information and spatial information between high-level and shallow-level features adaptively. Second, we propose a task-interactive head (TIH). It can reduce the task misalignment between classification and regression. Extensive experiments show that the improved detector obtains significant detection accuracy with considerable speed on two benchmark datasets.

关键词： geospatial object detection one-stage detector multi-scale balance learning task-interactive head

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On-Board Multi-Class geospatial object detection Based on Convolutional Neural Network for High Resolution Remote Sensing Images

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REMOTE SENSING 2023年第16期15卷 3963-3963页

作者： Shen, Yanyun Liu, Di Chen, Junyi Wang, Zhipan Wang, Zhe Zhang, Qingling Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen Campus66 Gongchang Rd Shenzhen 518107 Peoples R China Sun Yat Sen Univ Shenzhen Key Lab Intelligent Microsatellite Conste Shenzhen Campus66 Gongchang Road Shenzhen 518107 Peoples R China

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for object detection in Aerial Images-Cloud detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method.

关键词： on-board geospatial object detection cloud detection convolutional neural network high resolution remote sensing images

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geospatial object detection WITH SINGLE SHOT ANCHOR-FREE NETWORK

GEOSPATIAL OBJECT DETECTION WITH SINGLE SHOT ANCHOR-FREE NET...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Guo, Yiyou Ji, Jinsheng Lu, Xiankai Xie, Huan Tong, Xiaohua Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Intelligent Sensing & Recognit Shanghai 200240 Peoples R China Incept Inst Artificial Intelligence Abu Dhabi 5151 U Arab Emirates

ISBN: (纸本)9781728163741

geospatial object detection has made considerable progress with the use of anchor-based object detectors. In such a situation, the detection performance relies heavily on the parameter settings of anchor boxes. We present a Single Shot Anchor-Free Network (SSAFNet) to tackle with this problem. By eliminating the anchor boxes, the SSAFNet completely avoids the carefully predefined anchor boxes parameters and the computation for adapting the huge scale variation of geospatial objects. A compositional attention network is further introduced to enhance the saliency of foreground objects. We evaluate the SSAFNet on the representative NWPU VHR-10 and RSOD datasets, achieving competitive performance with state-of-the-art anchor-based detection methods.

关键词： Anchor-Free geospatial object detection Visual Attention

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geospatial object detection IN REMOTE SENSING IMAGES BASED ON MULTI-SCALE CONVOLUTIONAL NEURAL NETWORKS 39

GEOSPATIAL OBJECT DETECTION IN REMOTE SENSING IMAGES BASED O...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Yao, Qunli Hu, Xian Lei, Hong Chinese Acad Sci Inst Elect Dept Space Microwave Remote Sensing Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China

ISBN: (纸本)9781538691540

Automatic object detection is a basic but challenging problem in remote sensing images (RSIs) interpretation. Recently, a context-based top-down detection architecture has been proposed, which generates high-quality fusion features at all scales for object detection and significantly improves the accuracy of traditional detection framework. However, in the top-down architecture, small objects are easily lost in deep layers and the context cues will be weakened simultaneously. In this paper, to tackle these problems mentioned above, a novel Multi-scale detection Network (MSDN) is proposed. The proposed method maintains the resolution of deep features, which enhances the capability of multi-scale objects feature expression. Meanwhile, a dilated bottleneck structure is introduced, which effectively enlarges the receptive filed and improves the regression ability of multi-scale objects. The proposed method is evaluated on NWPU VHR-10 benchmarks and achieves impressive improvement over the comparable state-of-the-art detection framworks.

关键词： Remote sensing images geospatial object detection Convolutional Neural Network

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geospatial object detection in High Resolution Satellite Images Based on Multi-Scale Convolutional Neural Network

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REMOTE SENSING 2018年第1期10卷 131-131页

作者： Guo, Wei Yang, Wen Zhang, Haijian Hua, Guang Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China CETC Key Lab Aerosp Informat Applicat Shijiazhuang 050081 Hebei Peoples R China

Daily acquisition of large amounts of aerial and satellite images has facilitated subsequent automatic interpretations of these images. One such interpretation is object detection. Despite the great progress made in this domain, the detection of multi-scale objects, especially small objects in high resolution satellite (HRS) images, has not been adequately explored. As a result, the detection performance turns out to be poor. To address this problem, we first propose a unified multi-scale convolutional neural network (CNN) for geospatial object detection in HRS images. It consists of a multi-scale object proposal network and a multi-scale object detection network, both of which share a multi-scale base network. The base network can produce feature maps with different receptive fields to be responsible for objects with different scales. Then, we use the multi-scale object proposal network to generate high quality object proposals from the feature maps. Finally, we use these object proposals with the multi-scale object detection network to train a good object detector. Comprehensive evaluations on a publicly available remote sensing object detection dataset and comparisons with several state-of-the-art approaches demonstrate the effectiveness of the presented method. The proposed method achieves the best mean average precision (mAP) value of 89.6%, runs at 10 frames per second (FPS) on a GTX 1080Ti GPU.

关键词： high resolution satellite images geospatial object detection object proposal network object detection network

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Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2018年 138卷 281-294页

作者： Zhong, Yanfei Han, Xiaobing Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Hubei Peoples R China

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

关键词： geospatial object detection High spatial resolution (HSR) remote sensing imagery Residual network Pre-training mechanism Position-sensitive balancing

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A Single Shot Framework with Multi-Scale Feature Fusion for geospatial object detection

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REMOTE SENSING 2019年第5期11卷 594-594页

作者： Zhuang, Shuo Wang, Ping Jiang, Boran Wang, Gang Wang, Cong Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China CETC Key Lab Aerosp Informat Applicat Shijiazhuang 050081 Hebei Peoples R China

With the rapid advances in remote-sensing technologies and the larger number of satellite images, fast and effective object detection plays an important role in understanding and analyzing image information, which could be further applied to civilian and military fields. Recently object detection methods with region-based convolutional neural network have shown excellent performance. However, these two-stage methods contain region proposal generation and object detection procedures, resulting in low computation speed. Because of the expensive manual costs, the quantity of well-annotated aerial images is scarce, which also limits the progress of geospatial object detection in remote sensing. In this paper, on the one hand, we construct and release a large-scale remote-sensing dataset for geospatial object detection (RSD-GOD) that consists of 5 different categories with 18,187 annotated images and 40,990 instances. On the other hand, we design a single shot detection framework with multi-scale feature fusion. The feature maps from different layers are fused together through the up-sampling and concatenation blocks to predict the detection results. High-level features with semantic information and low-level features with fine details are fully explored for detection tasks, especially for small objects. Meanwhile, a soft non-maximum suppression strategy is put into practice to select the final detection results. Extensive experiments have been conducted on two datasets to evaluate the designed network. Results show that the proposed approach achieves a good detection performance and obtains the mean average precision value of 89.0% on a newly constructed RSD-GOD dataset and 83.8% on the Northwestern Polytechnical University very high spatial resolution-10 (NWPU VHR-10) dataset at 18 frames per second (FPS) on a NVIDIA GTX-1080Ti GPU.

关键词： geospatial object detection remote-sensing images multi-scale detector feature fusion

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geospatial object detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics

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REMOTE SENSING 2018年第6期10卷 820-820页

作者： Chen, Shiqi Zhan, Ronghui Zhang, Jun Natl Univ Def Technol Sci & Technol Automat Target Recognit Lab Changsha 410073 Hunan Peoples R China

geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a heated and challenging problem in the field of automatic image interpretation. Despite convolutional neural networks (CNNs) having facilitated the development in this domain, the computation efficiency under real-time application and the accurate positioning on relatively small objects in HSR images are two noticeable obstacles which have largely restricted the performance of detection methods. To tackle the above issues, we first introduce semantic segmentation-aware CNN features to activate the detection feature maps from the lowest level layer. In conjunction with this segmentation branch, another module which consists of several global activation blocks is proposed to enrich the semantic information of feature maps from higher level layers. Then, these two parts are integrated and deployed into the original single shot detection framework. Finally, we use the modified multi-scale feature maps with enriched semantics and multi-task training strategy to achieve end-to-end detection with high efficiency. Extensive experiments and comprehensive evaluations on a publicly available 10-class object detection dataset have demonstrated the superiority of the presented method.

关键词： high spatial resolution (HSR) remote sensing images geospatial object detection segmentation semantic information

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ROBUST geospatial object detection BASED ON PRE-TRAINED FASTER R-CNN FRAMEWORK FOR HIGH SPATIAL RESOLUTION IMAGERY 37

ROBUST GEOSPATIAL OBJECT DETECTION BASED ON PRE-TRAINED FAST...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Han, Xiaobing Zhong, Yanfei Feng, Ruyi Zhang, Liangpei Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Hubei Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol Wuhan 430079 Hubei Peoples R China China Univ Geosci Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781509049516

geospatial object detection from high spatial resolution (HSR) imagery is significant and challenging for further analyzing the object-related information in various civil and military applications. Traditional object detection methods based on the handcrafted features are limited by their efficiency in describing the multi-class objects from large swath and complex-context HSR imagery. Although convolutional neural network (CNN) can extract the features automatically, the feature extraction and detection stages are still separate and time-consuming. In addition, manual labelling information is limited and an efficient real-time one-stage detection framework for HSR imagery is scare. In this paper, a robust pre-trained efficient multi-class geospatial object detection framework pre-trained Faster R-CNN sharing the convolutional features between region proposal stage and detection stage is proposed for HSR imagery. Extensive experiments and evaluations on a ten class object detection dataset are conducted for the proposed method.

关键词： geospatial object detection HSR imagery Faster RCNN real-time feature sharing

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