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Hidenari Honda - IEEE Xplore Author Profile

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Author details

Hidenari Honda

Publications

2

Citations

79

Publications by Year

20092013

Co-Authors:

Bryan AlmondAnders AnderssonTimothy J. BalesJoe BrooksTony Chow

Show All Co-Authors (21)

Hidenari Honda

Affiliation

Aptina Japan

LLC

Minato, Tokyo, Japan

Publication Topics

Image Sensor,
Power Consumption,
Readout Noise,
Analog-to-digital Converter,
Bitrate,
Conversion Gain,
Conversion Time,
Datapath,
Differential Output,
Digital Block,
Digital Domain,
Digital Operations

Biography

Hidenari Honda received the B.E. degree in electronics from Fukuoka University, Fukuoka, Japan, in 1999.,He joined Nippon Systemware Company Ltd. in 1999 and was engaged in the digital design of image processors for digital still cameras. In 2003, he joined Olympus Corporation and was engaged in the digital design of image processors for digital still cameras and endoscopes. Since 2007, he has been with Aptina Japan, LLC, Tokyo, Japan.(Based on document published on 6 October 2009).

Publications

2

Citations

79

Publications by Year

20092013

Co-Authors:

Bryan Almond
Anders Andersson
Timothy J. Bales
Joe Brooks
Tony Chow

Show All Co-Authors (21)

Author's Published Works

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Showing 1-14 of 14 results

Journals (13)

Conferences (1)

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Licheng Jiao(14)
Zhongle Ren(14)
Biao Hou(13)
Bo Ren(4)
Hao Zhu(4)
Key Laboratory of Intelligent Perception and Image Understanding, Ministry of Education of China, and the Joint International Research Laboratory of Intelligent Perception and Computation, Xidian University, Xi’an, China(3)
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an, Shaanxi, China(2)
Key Laboratory of Information Fusion Technology of Ministry of Education, Northwestern Polytechnical University, Xi’an, China(2)
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi’an, China(2)
Hangzhou Institute of Technology, Xidian University, Hangzhou, China(1)
IEEE Transactions on Geoscience and Remote Sensing(11)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing(2)
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium(1)
IEEE(14)
Yokohama, Japan(1)
Convolutional Neural Network(7)
Classification Accuracy(5)
Classification Performance(5)
Synthetic Aperture Radar(5)
Feature Learning(4)

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Results

Interactive Concept Network Enhanced Transformer for Remote Sensing Image Captioning

Cheng Zhang;Zhongle Ren;Biao Hou;Jianhua Meng;Weibin Li;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

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Remote sensing image captioning plays an important role in advancing remote sensing image understanding with natural language generation. However, it is difficult to generate accurate semantic descriptions of crucial objects and their relationships, due to large coverage and abundant information in remote sensing images. To address these issues, this article proposes a novel interactive concept ne...Show More

Interactive Concept Network Enhanced Transformer for Remote Sensing Image Captioning

Cheng Zhang;Zhongle Ren;Biao Hou;Jianhua Meng;Weibin Li;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2025 | Volume: 63 | Journal Article |

Water-Matching CAM: A Novel Class Activation Map for Weakly-Supervised Semantic Segmentation of Water in SAR Images

Kai Wang;Zhongle Ren;Biao Hou;Feng Sha;Zhiyang Wang;Weibin Li;Licheng Jiao

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2025 | Volume: 18 | Journal Article |

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Recently, semantic segmentation of water in synthetic aperture radar (SAR) images has attracted the attention of more and more scholars. However, existing methods usually require many accurate manually labeled pixel-level water annotations of SAR images, which leads to the problem that they are often time-consuming and costly. To mitigate this problem, we apply the weakly-supervised semantic segme...Show More

Water-Matching CAM: A Novel Class Activation Map for Weakly-Supervised Semantic Segmentation of Water in SAR Images

Kai Wang;Zhongle Ren;Biao Hou;Feng Sha;Zhiyang Wang;Weibin Li;Licheng Jiao

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2025 | Volume: 18 | Journal Article |

Rebalancing Gaussian Location Loss for High-Precision Detection on Remote Sensing Images

Zhonghua Li;Biao Hou;Zitong Wu;Xianpeng Guo;Bo Ren;Zhongle Ren;Chen Yang;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

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Aerial image objects are usually orientated arbitrarily, with a large scale range, and densely distributed. Traditional horizontal bounding box (HBB) detectors tend to filter out densely distributed objects leading to missed detections, such as ship (SH) and vehicle. Therefore, oriented object detection has become a mainstream solution in recent years. The 2-D Gaussian distribution representation ...Show More

Rebalancing Gaussian Location Loss for High-Precision Detection on Remote Sensing Images

Zhonghua Li;Biao Hou;Zitong Wu;Xianpeng Guo;Bo Ren;Zhongle Ren;Chen Yang;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

A Semantically Nonredundant Continuous-Scale Feature Network for Panchromatic and Multispectral Classification

Hao Zhu;Wenhao Zhao;Xiaotong Li;Biao Hou;Changzhe Jiao;Zhongle Ren;Wenping Ma;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (1)

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In recent years, panchromatic (PAN) images and multispectral (MS) images, as a type of multimodal remote sensing data, are attracting increasingly more attention to their classification problems. However, effectively representing size variations of targets in remote sensing images and reducing redundant representations of different modalities’ deep semantic features to enhance classification accur...Show More

A Semantically Nonredundant Continuous-Scale Feature Network for Panchromatic and Multispectral Classification

Hao Zhu;Wenhao Zhao;Xiaotong Li;Biao Hou;Changzhe Jiao;Zhongle Ren;Wenping Ma;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Self-Supervised Learning Guided by SAR Image Factors for Terrain Classification

Zhongle Ren;Zhe Du;Shaobo Liu;Biao Hou;Weibin Li;Hao Zhu;Bo Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

Cited by: Papers (1)

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Effective feature representation is the key to synthetic aperture radar (SAR) image terrain classification. Limited by the abstract appearance and the scarcity of high-quality labeled data in this field, the features learned by current methods, especially deep learning models, do not have enough directivity and applicability, which hampers the performance. This article proposes multi-image factor ...Show More

Self-Supervised Learning Guided by SAR Image Factors for Terrain Classification

Zhongle Ren;Zhe Du;Shaobo Liu;Biao Hou;Weibin Li;Hao Zhu;Bo Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2024 | Volume: 62 | Journal Article |

A Dual-Stream Transformer With Diff-Attention for Multispectral and Panchromatic Classification

Lin Xu;Hao Zhu;Licheng Jiao;Wenhao Zhao;Xiaotong Li;Biao Hou;Zhongle Ren;Wenping Ma

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (3)

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To minimize the feature redundancy of multispectral (MS) and panchromatic (PAN) images and maximize the complementary advantages of PAN and MS, a dual-stream transformer with diff-attention (DSTD)-Net is proposed for PAN and MS classification in this article. First, in terms of feature extraction, we use self-attention and coattention (SCA) block to extract both specific advantageous features and ...Show More

A Dual-Stream Transformer With Diff-Attention for Multispectral and Panchromatic Classification

Lin Xu;Hao Zhu;Licheng Jiao;Wenhao Zhao;Xiaotong Li;Biao Hou;Zhongle Ren;Wenping Ma

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Gaussian Synthesis for High-Precision Location in Oriented Object Detection

Zhonghua Li;Biao Hou;Zitong Wu;Bo Ren;Zhongle Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

Cited by: Papers (8)

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In aerial image scenes, the objects have properties of arbitrary orientation, large-scale range, and dense distribution. Thus, the object detector uses an oriented bounding box (OBB) to locate objects, which is more complex and challenging than a horizontal bounding box (HBB) detector. Mainstream OBB detectors mostly use a one-to-many label assignment strategy to predict multiple bounding boxes fo...Show More

Gaussian Synthesis for High-Precision Location in Oriented Object Detection

Zhonghua Li;Biao Hou;Zitong Wu;Bo Ren;Zhongle Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2023 | Volume: 61 | Journal Article |

A Collaborative Correlation-Matching Network for Multimodality Remote Sensing Image Classification

Wenping Ma;Na Li;Hao Zhu;Kenan Sun;Zhongle Ren;Xu Tang;Biao Hou;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (9)

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Recently, with the increasing availability of the high-quality panchromatic (PAN) and multispectral (MS) remote sensing (RS) images, the inherent complementarity between PAN and MS images provides a wide development prospect for the multimodality RS image classification task. However, how to cleverly relieve the modal differences and effectively integrate the single-modality PAN and MS features is...Show More

A Collaborative Correlation-Matching Network for Multimodality Remote Sensing Image Classification

Wenping Ma;Na Li;Hao Zhu;Kenan Sun;Zhongle Ren;Xu Tang;Biao Hou;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Network Pruning for Remote Sensing Images Classification Based on Interpretable CNNs

Xianpeng Guo;Biao Hou;Bo Ren;Zhongle Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cited by: Papers (22)

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Convolutional neural network (CNN)-based research has been successfully applied in remote sensing image classification due to its powerful feature representation ability. However, these high-capacity networks bring heavy inference costs and are easily overparameterized, especially for the deep CNNs pretrained on natural image datasets. Network pruning is regarded as a prevalent approach for compre...Show More

Network Pruning for Remote Sensing Images Classification Based on Interpretable CNNs

Xianpeng Guo;Biao Hou;Bo Ren;Zhongle Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2022 | Volume: 60 | Journal Article |

Cost-Sensitive Latent Space Learning for Imbalanced PolSAR Image Classification

Qian Wu;Biao Hou;Zaidao Wen;Zhongle Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2021 | Volume: 59, Issue: 6 | Journal Article |

Cited by: Papers (16)

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Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) image interpretation. The classification performance of a promising parametric feature and classifier learning-based algorithm is limited when the amounts of pixels from different classes vary greatly. PolSAR data from minority classes is difficult to recognize correctly owing to a strong learn...Show More

Cost-Sensitive Latent Space Learning for Imbalanced PolSAR Image Classification

Qian Wu;Biao Hou;Zaidao Wen;Zhongle Ren;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2021 | Volume: 59, Issue: 6 | Journal Article |

A Distribution and Structure Match Generative Adversarial Network for SAR Image Classification

Zhongle Ren;Biao Hou;Qian Wu;Zaidao Wen;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 6 | Journal Article |

Cited by: Papers (34)

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Synthetic aperture radar (SAR) image classification is a fundamental research in the interpretation of SAR images. The previous methods are unilaterally based on statistical features or spatial features, which cannot capture features with complete SAR image characteristics and unavoidably limits the performance for classification. In this article, novel sample weighting and class adversarial train...Show More

A Distribution and Structure Match Generative Adversarial Network for SAR Image Classification

Zhongle Ren;Biao Hou;Qian Wu;Zaidao Wen;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 6 | Journal Article |

Object Detection in High-Resolution Panchromatic Images Using Deep Models and Spatial Template Matching

Biao Hou;Zhongle Ren;Wei Zhao;Qian Wu;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 2 | Journal Article |

Cited by: Papers (30)

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Automatic object detection from remote sensing images has attracted a significant attention due to its importance in both military and civilian fields. However, the low confidence of the candidates restricts the recognition of potential objects, and the unreasonable predicted boxes result in false positives (FPs). To address these issues, an accurate and fast object detection method called the ref...Show More

Object Detection in High-Resolution Panchromatic Images Using Deep Models and Spatial Template Matching

Biao Hou;Zhongle Ren;Wei Zhao;Qian Wu;Licheng Jiao

IEEE Transactions on Geoscience and Remote Sensing

Year: 2020 | Volume: 58, Issue: 2 | Journal Article |

Pixel Dag-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification

Xiufang Li;Qigong Sun;Lingling Li;Zhongle Ren;Fang Liu;Licheng Jiao

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Cited by: Papers (2)

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Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs). In this paper, based on the mechanism of the population receptive field (pRF) in human visual cortex, we further utilize the spatial correlation of pixels in images and propose pixel directed acyclic graph recurrent neural network (Pixel DAG-RNN) to extract and apply spe...Show More

Pixel Dag-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification

Xiufang Li;Qigong Sun;Lingling Li;Zhongle Ren;Fang Liu;Licheng Jiao

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

Year: 2019 | Conference Paper |

Patch-Sorted Deep Feature Learning for High Resolution SAR Image Classification

Zhongle Ren;Biao Hou;Zaidao Wen;Licheng Jiao

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2018 | Volume: 11, Issue: 9 | Journal Article |

Cited by: Papers (32)

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Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. The traditional SAR classification methods extract shallow and handcrafted features, which cannot subtly depict the abundant modal information in high resolution SAR image. Inspired by deep learning, an effective feature learning tool, a novel method called patch-sorted deep...Show More

Patch-Sorted Deep Feature Learning for High Resolution SAR Image Classification

Zhongle Ren;Biao Hou;Zaidao Wen;Licheng Jiao

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Year: 2018 | Volume: 11, Issue: 9 | Journal Article |

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