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A new ship detection method for massive data high-resolution remote sensing images

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2012 2nd International Conference on Materials Science and information technology, MSIT 2012

作者： Sun, Hao Li, Yu Liu, Ge Long, Hui Wang, Hongqi Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-spatial Information Processing and Application System Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Graduate University Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9783037854389

This paper proposes a new method for automatic ship targets detection in remote sensing images. The method uses adaptive segmentation algorithm for getting possible ship targets first, and then calculates Histograms of Oriented Gradient (HOG) feature to extract the structural information of ships, followed by supervised learning algorithm to identify the possible ship targets. Multi-scale sliding-window is used to handle targets with different scales. The experimental results prove that this new method has a good precision and robustness for most of the ship targets and give attention to the efficiency.© (2012) Trans Tech Publications, Switzerland.

关键词： Ships

TOPOGRAPHIC CORRECTION OF ETM IMAGES BASED ON SMOOTHED TERRAIN

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Journal of Electronics(China) 2012年第3期29卷 271-278页

作者： Jiang Kang Zhao Yongchao Geng Xiurui Tang Hairong Key Laboratory of Technology in Geo-spatial Information Processing and Application Systems Beijing 100100China Institute of Electronics Chinese Academy of SciencesBeijing 1001P0China Graduate University Chinese Academy of SciencesBeijing 100040China

A new empirical topographic correction method is proposed in this paper. The main idea of the new method is smoothing the slope angle of terrain in the first place and then performing the cosine correction based on the smoothed terrain. A comparison is conducted among the new method and several other common methods using Landsat-7 ETM+ data. Visual analysis and statistical analysis are adopted to assess the performance of these methods from two aspects: overcorrection, homogeneity within a land cover class. Comparison results indicate that the new method is superior to the cosine correction, Gamma correction, Sun-Canopy-Sensor correction, and Minnaert correction. Compared with common methods, the proposed one can eliminate overcorrection better and is an effective topographic correction method.

关键词： Topographic correction Landsat-7 ETM Smoothed terrain

Advancing Few-Shot Instance Segmentation of Military Aircraft in Remote Sensing Imagery with Large Vision Models

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Advancing Few-Shot Instance Segmentation of Military Aircraf...

IEEE International Symposium on geoscience and Remote Sensing (IGARSS)

作者： Xiyu Qi Yifan Wu Wenhui Zhang Zhan Chen Yidan Zhang Lei Wang Aerospace Information Research Institute Chinese Academy of Sciences Key Laboratory of Target Cognition and Application Technology (TCAT) Key Laboratory of Network Information System Technology (NIST) Key Laboratory of Technology in Geo-Spatial Information Processing and Application System

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study introduces a novel few-shot instance segmentation framework MAR FSIS for identifying and segmenting aircraft in high-resolution remote sensing imagery under stringent few-shot conditions. Utilizing large vision models in conjunction with a sophisticated detector and a novel object prompter, MAR FSIS demonstrates superior performance in discerning intricate details of aircraft with minimal training examples. The effectiveness of this approach is confirmed through experimental evaluations on the proposed MAR20IS dataset, achieving remarkable precision in both detection and segmentation tasks.

关键词： Instance segmentation Training Atmospheric modeling Detectors Military aircraft Data models Proposals

Research on statistical analysis of SAR images

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Research on statistical analysis of SAR images

2009 Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2009

作者： Zhan, Qian-Qian You, Hong-Jian Hong, Wen Institute of Electronics Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing 100190 China Graduate School of the Chinese Academy of Sciences Beijing 100039 China National Key Laboratory of Microwave Imaging Technology Beijing 100190 China

ISBN: (纸本)9781424427314

The statistic distribution of SAR image plays an important role in SAR image processing, and it has a great influence on the results. In practical applications, the distribution is often specified by human through experience, but in fact the distribution changes with different pictures of different scenes. In this paper we first find a way to determine the form of the image distribution with Pearson system, and then we use the Chebyshev-Hermite polynomials and the statistical properties of SAR image to get the Edgeworth expansion. Finally, we practice the distribution simulation on four different types of scene (the ocean, the town, the mountain and the agricultural fields). The experimental results prove the efficiency and the accuracy of these two methods and will lay a good foundation for the follow-up steps in image processing. ©2009 IEEE.

关键词： Synthetic aperture radar

A novel proposal of Gaofen-3 satellite constellation for multi-applications

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A novel proposal of Gaofen-3 satellite constellation for mul...

ISPRS geospatial Week 2017

作者： Qiu, Xiaolan Ding, Chibiao Lei, Bin Han, Bing Li, Fangfang Key Laboratory of Geo-Spatial Information Processing and Application System Chinese Academy of Sciences Beijing China Institute of Electronics Chinese Academy of Sciences Suzhou Research Center Suzhou China Institute of Electronics Chinese Academy of Sciences Beijing China

Gaofen-3 is the first C-band fully polarimetric SAR satellite in China, which is widely used in various fields such as ocean monitoring, disaster reduction and so on. In this paper, a new satellite constellation is proposed based on the orbit of Gaofen-3 satellite. The constellation includes Gaofen-3 and other two duplicates. It is able to do repeat-pass interferometry, repeat-pass differential interferometry, along-track interferometry and stereo measurement. With these abilities, it can generate the earth DEM without ground control points and have better performance in moving target identification and monitoring. The performance and the system requirements are analysed, which provides a good reference for the design of spaceborne SAR constellation.

关键词： Synthetic aperture radar

A coarse-to-fine framework for learned color enhancement with non-local attention

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arXiv 2019年

作者： Shan, Chaowei Zhang, Zhizheng Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Automatic color enhancement is aimed to adaptively adjust photos to expected styles and tones. For current learned methods in this field, global harmonious perception and local details are hard to be well-considered in a single model simultaneously. To address this problem, we propose a coarse-tofine framework with non-local attention for color enhancement in this paper. Within our framework, we propose to divide enhancement process into channel-wise enhancement and pixel-wise refinement performed by two cascaded Convolutional Neural Networks (CNNs). In channel-wise enhancement, our model predicts a global linear mapping for RGB channels of input images to perform global style adjustment. In pixel-wise refinement, we learn a refining mapping using residual learning for local adjustment. Further, we adopt a non-local attention block to capture the long-range dependencies from global information for subsequent fine-grained local refinement. We evaluate our proposed framework on the commonly using benchmark and conduct sufficient experiments to demonstrate each technical component within it. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

Domain-class correlation decomposition for generalizable person re-identification

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arXiv 2021年

作者： Yang, Kaiwen Tian, Xinmei CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Domain generalization in person re-identification is a highly important meaningful and practical task in which a model trained with data from several source domains is expected to generalize well to unseen target domains. Domain adversarial learning is a promising domain generalization method that aims to remove domain information in the latent representation through adversarial training. However, in person re-identification, the domain and class are correlated, and we theoretically show that domain adversarial learning will lose certain information about class due to this domain-class correlation. Inspired by casual inference, we propose to perform interventions to the domain factor d, aiming to decompose the domain-class correlation. To achieve this goal, we proposed estimating the resulting representation z∗ caused by the intervention through first- and second-order statistical characteristic matching. Specifically, we build a memory bank to restore the statistical characteristics of each domain. Then, we use the newly generated samples {z∗, y, d∗} to compute the loss function. These samples are domain-class correlation decomposed;thus, we can learn a domain-invariant representation that can capture more class-related features. Extensive experiments show that our model outperforms the state-of-the-art methods on the large-scale domain generalization Re-ID benchmark. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

No-reference light field image quality assessment based on micro-lens image

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arXiv 2019年

作者： Luo, Ziyuan Zhou, Wei Shi, Likun Chen, Zhibo CAS Key Laboratory of Technology in Geo-spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Light field image quality assessment (LF-IQA) plays a significant role due to its guidance to Light Field (LF) contents acquisition, processing and application. The LF can be represented as 4-D signal, and its quality depends on both angular consistency and spatial quality. However, few existing LF-IQA methods concentrate on effects caused by angular inconsistency. Especially, no-reference methods lack effective utilization of 2-D angular information. In this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can simultaneously record the angular information in both horizontal and vertical directions. Since the MLI contains 2-D angular information, we propose a No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI). Specifically, we first utilize Global Entropy Distribution (GED) and Uniform Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and then pool them together to measure angular consistency. In addition, the information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial quality. Extensive experimental results show that LF-QMLI achieves the state-of-the-art performance. Copyright © 2019, The Authors. All rights reserved.

关键词： Image quality

FreqAlign: Excavating Perception-oriented Transferability for Blind Image Quality Assessment from A Frequency Perspective

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arXiv 2023年

作者： Li, Xin Lu, Yiting Chen, Zhibo The CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application System University of Science and Technology of China Hefei230027 China

Blind Image Quality Assessment (BIQA) is susceptible to poor transferability when the distribution shift occurs, e.g., from synthesis degradation to authentic degradation. To mitigate this, some studies have attempted to design unsupervised domain adaptation (UDA) based schemes for BIQA, which intends to eliminate the domain shift through adversarial-based feature alignment. However, the feature alignment is usually taken at the low-frequency space of features since the global average pooling operation. This ignores the transferable perception knowledge in other frequency components and causes the sub-optimal solution for the UDA of BIQA. To overcome this, from a novel frequency perspective, we propose an effective alignment strategy, i.e., Frequency Alignment (dubbed FreqAlign), to excavate the perception-oriented transferability of BIQA in the frequency space. Concretely, we study what frequency components of features are more proper for perception-oriented alignment. Based on this, we propose to improve the perception-oriented transferability of BIQA by performing feature frequency decomposition and selecting the frequency components that contained the most transferable perception knowledge for alignment. To achieve a stable and effective frequency selection, we further propose the frequency movement with a sliding window to find the optimal frequencies for alignment, which is composed of three strategies, i.e., warm up with pre-training, frequency movement-based selection, and perturbation-based finetuning. Extensive experiments under different domain adaptation settings of BIQA have validated the effectiveness of our proposed method. The code will be released at https://***/lixinustc/Openworld-IQA. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment