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Found 26 papers, 11 papers with code
OBSUM: An object-based spatial unmixing model for spatiotemporal fusion of remote sensing images
Furthermore, OBSUM also achieved satisfactory results in two typical remote sensing applications.
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
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).
Incomplete Multimodal Learning for Remote Sensing Data Fusion
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.
Unsupervised CD in satellite image time series by contrastive learning and feature tracking
In this work, we propose a two-stage approach to unsupervised change detection in satellite image time-series using contrastive learning with feature tracking.
Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
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
Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
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.
Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs.
Self-supervised Remote Sensing Images Change Detection at Pixel-level
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.
Recent Advances in Domain Adaptation for the Classification of Remote Sensing Data
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.
Robust Registration of Multimodal Remote Sensing Images Based on Structural Similarity
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.
Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification
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.
Self-supervised Change Detection in Multi-view Remote Sensing Images
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.
Self-supervised SAR-optical Data Fusion and Land-cover Mapping using Sentinel-1/-2 Images
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.
Adversarial Shape Learning for Building Extraction in VHR Remote Sensing Images
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.
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
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)
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images.
Remote Sensing Image Scene Classification with Deep Neural Networks in JPEG 2000 Compressed Domain
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.
DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images
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.)
Improving Semantic Segmentation of Aerial Images Using Patch-based Attention
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.
Multisource and Multitemporal Data Fusion in Remote Sensing
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.
Tensor Alignment Based Domain Adaptation for Hyperspectral Image Classification
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.
Cross-Domain Collaborative Learning via Cluster Canonical Correlation Analysis and Random Walker for Hyperspectral Image Classification
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.
Fast and Robust Matching for Multimodal Remote Sensing Image Registration
To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration.
Recent Developments from Attribute Profiles for Remote Sensing Image Classification
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.
Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery
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.
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale or color photographic images.
