1 code implementation • 14 Oct 2023 • Houcai Guo, Dingqi Ye, Lorenzo Bruzzone
Furthermore, OBSUM also achieved satisfactory results in two typical remote sensing applications.
1 code implementation • 4 Sep 2023 • Lei Ding, Kun Zhu, Daifeng Peng, Hao Tang, Kuiwu Yang, Lorenzo Bruzzone
In this work, we aim to utilize the strong visual recognition capabilities of VFMs to improve the change detection of high-resolution Remote Sensing Images (RSIs).
no code implementations • 22 Apr 2023 • Yuxing Chen, Lorenzo Bruzzone
In this work, we propose a two-stage approach to unsupervised change detection in satellite image time-series using contrastive learning with feature tracking.
no code implementations • 22 Apr 2023 • Yuxing Chen, Maofan Zhao, Lorenzo Bruzzone
The mechanism of connecting multimodal signals through self-attention operation is a key factor in the success of multimodal Transformer networks in remote sensing data fusion tasks.
1 code implementation • 10 Dec 2022 • Lei Ding, Jing Zhang, Kai Zhang, Haitao Guo, Bing Liu, Lorenzo Bruzzone
Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Images (RSIs).
Ranked #1 on Change Detection on SECOND
1 code implementation • 13 Aug 2021 • Lei Ding, Haitao Guo, Sicong Liu, Lichao Mou, Jing Zhang, Lorenzo Bruzzone
Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch.
1 code implementation • 29 Jun 2021 • Lei Ding, Dong Lin, Shaofu Lin, Jing Zhang, Xiaojie Cui, Yuebin Wang, Hao Tang, Lorenzo Bruzzone
To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs.
no code implementations • 18 May 2021 • Yuxing Chen, Lorenzo Bruzzone
To overcome the effects of regular seasonal changes in binary change maps, we also used an uncertainty method to enhance the temporal robustness of the proposed approach.
no code implementations • 15 Apr 2021 • Devis Tuia, Claudio Persello, Lorenzo Bruzzone
The success of supervised classification of remotely sensed images acquired over large geographical areas or at short time intervals strongly depends on the representativity of the samples used to train the classification algorithm and to define the model.
no code implementations • 31 Mar 2021 • Yuanxin Ye, Jie Shan, Lorenzo Bruzzone, Li Shen
Moreover, a robust registration method is also proposed in this paper based on HOPCncc, which is evaluated using six pairs of multimodal remote sensing images.
1 code implementation • 15 Mar 2021 • Lichao Mou, Sudipan Saha, Yuansheng Hua, Francesca Bovolo, Lorenzo Bruzzone, Xiao Xiang Zhu
To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning.
1 code implementation • 10 Mar 2021 • Yuxing Chen, Lorenzo Bruzzone
In this approach, a pseudo-Siamese network is trained to regress the output between its two branches pre-trained in a contrastive way on a large dataset of multi-temporal homogeneous or heterogeneous image patches.
no code implementations • 9 Mar 2021 • Yuxing Chen, Lorenzo Bruzzone
For the land-cover mapping task, we assign each pixel a land-cover class by the joint use of pre-trained features and spectral information of the image itself.
1 code implementation • 22 Feb 2021 • Lei Ding, Hao Tang, Yahui Liu, Yilei Shi, Xiao Xiang Zhu, Lorenzo Bruzzone
To address this issue, we propose an adversarial shape learning network (ASLNet) to model the building shape patterns that improve the accuracy of building segmentation.
1 code implementation • 24 Dec 2020 • Swalpa Kumar Roy, Suvojit Manna, Tiecheng Song, Lorenzo Bruzzone
Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands.
Ranked #1 on Hyperspectral Image Classification on Kennedy Space Center (Overall Accuracy metric)
1 code implementation • 10 Nov 2020 • Lei Ding, Kai Zheng, Dong Lin, Yuxing Chen, Bing Liu, Jiansheng Li, Lorenzo Bruzzone
This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images.
no code implementations • 20 Jun 2020 • Akshara Preethy Byju, Gencer Sumbul, Begüm Demir, Lorenzo Bruzzone
This is achieved by taking codestreams associated with the coarsest resolution wavelet sub-band as input to approximate finer resolution sub-bands using a number of transposed convolutional layers.
1 code implementation • 14 May 2020 • Lei Ding, Lorenzo Bruzzone
The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.)
no code implementations • 20 Nov 2019 • Lei Ding, Hao Tang, Lorenzo Bruzzone
High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurred spatial details; low-level features extracted from the early layers of a network contain more pixel-level information, but are isolated and noisy.
no code implementations • 19 Dec 2018 • Pedram Ghamisi, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo, Mingmin Chi, Katharina Anders, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data.
no code implementations • 29 Aug 2018 • Yao Qin, Lorenzo Bruzzone, Biao Li
Then we consider the subspace invariance between two domains as projection matrices and original tensors are projected as core tensors with lower dimensions into the invariant tensor subspace by applying Tucker decomposition.
no code implementations • 29 Aug 2018 • Yao Qin, Lorenzo Bruzzone, Biao Li, Yuanxin Ye
To be specific, the proposed CDCL method is an iterative process of three main stages, i. e. twice of RW-based pseudolabeling and cross domain learning via C-CCA.
no code implementations • 19 Aug 2018 • Yuanxin Ye, Lorenzo Bruzzone, Jie Shan, Francesca Bovolo, Qing Zhu
To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration.
no code implementations • 27 Mar 2018 • Minh-Tan Pham, Sébastien Lefèvre, Erchan Aptoula, Lorenzo Bruzzone
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task.
no code implementations • 7 Mar 2018 • Lichao Mou, Lorenzo Bruzzone, Xiao Xiang Zhu
As far as we know, this is the first time that a recurrent convolutional network architecture has been proposed for multitemporal remote sensing image analysis.
no code implementations • 18 Oct 2013 • Gustavo Camps-Valls, Devis Tuia, Lorenzo Bruzzone, Jón Atli Benediktsson
Hyperspectral images show similar statistical properties to natural grayscale or color photographic images.