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Interactive Concept Network Enhanced Transformer for Remote Sensing Image Captioning
Cheng Zhang;Zhongle Ren;Biao Hou;Jianhua Meng;Weibin Li;Licheng Jiao
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
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
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
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
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
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
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
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
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
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
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
Gaussian Synthesis for High-Precision Location in Oriented Object Detection
Zhonghua Li;Biao Hou;Zitong Wu;Bo Ren;Zhongle Ren;Licheng Jiao
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
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
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
Network Pruning for Remote Sensing Images Classification Based on Interpretable CNNs
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
Cost-Sensitive Latent Space Learning for Imbalanced PolSAR Image Classification
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
A Distribution and Structure Match Generative Adversarial Network for SAR Image Classification
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
Object Detection in High-Resolution Panchromatic Images Using Deep Models and Spatial Template Matching
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
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)
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
Patch-Sorted Deep Feature Learning for High Resolution SAR Image Classification
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
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