Automatic ship segmentation from high-resolution Synthetic Aperture Radar (SAR) remote sensing images has been a topic of interest that has gradually gained attention over the years due to the abundance of earth observation sensors. Recently, deep learning methods have provided a breakthrough increasing the performance greatly by using large amount of labeled data. Yet, the high cost related to the samples labeling and their scarcity result in significant limitation of their wide use. Therefore, it is crucial to overcome the unlabeled inputs challenge and develop semi-supervised learning approaches to enhance the machine learning models capacity. Our letter proposes a semi-supervised segmentation algorithm for SAR images named SemiSegSAR based on the use of Graph Signal Processing. This method includes instance segmentation; texture and statistical SAR features to represent the nodes of the graph; K-nearest neighbors to construct the graph; and Sobolev minimization algorithm to tackle the problem of semi-supervised semantic segmentation. The proposed algorithm is trained and tested using the publicly available SSDD and HRSID ship detection datasets. Experiments show that SemiSegSAR outperforms the current state-of-the-art semi-supervised and supervised methods while requiring only few labeled data.

Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervised learning approaches based on graph signal processing emerged in computer vision due to their ability to avail the geometrical structure of data. Video object segmentation can be considered as a node classification problem. In this paper, we propose a new approach called GraphECV based on the use of graph signal processing for semi-supervised learning of video object segmentation applied for the segmentation of the left ventricle in echordiography videos. GraphECV includes instance segmentation, extraction of temporal, texture and statistical features to represent the nodes, construction of a graph using K-nearest neighbors, graph sampling to embed the graph with small amount of labeled nodes or graph signals, and finally a semi-supervised learning approach based on the minimization of the Sobolov norm of graph signals. The new algorithm is evaluated using two publicly available echocardiography videos, EchoNet-Dynamic and CAMUS datasets. The proposed approach outperforms other state-of-the-art methods under challenging background conditions.

The detection of natural disasters is a critical step in supporting Sustainable Development Goal 15. In this paper, we introduce a novel segmentation method to identify the areas affected by floods and landslides, using multiple sources such as multispectral imagery, Synthetic Aperture Radar data, Digital Elevation Models, slope, and hydrology maps. Traditional deep learning semantic segmentation algorithms encounter difficulties in handling multi-source data. In response to this challenge, we have refined the single-channel encoder-decoder architecture to better discern features derived from multiple sources. We introduce Multi-Source Semantic Segmentation with Channel and Spatial Attention Fusion (MSSCSAF-Net), a novel solution for semantic segmentation. MSSCSAF-Net incorporates a multi-source encoder designed to extract features from diverse inputs. This technique involves the addition of a Multi-Source Channel Spatial Attention Fusion Module to fuse the extracted features, and a Decoder Channel Spatial Attention Fusion Module to improve the fusion with attention of spatial and abstract features. The evaluation on the MMFlood and Landslide4Sense datasets finds that our approach exceeds the performance of current state-of-the-art segmentation techniques.

Object detection in remote sensing images has been a topic of interest that has gradually gained attention over the years due to the wide variety of related applications. Even though there is an extensive number of methods developed for object detection, there are still several challenges that remain unsolved, such as visual appearance variations, occlusions, and background clutter. Satellite images reveal a texture problem; it is difficult to differentiate between the background and the object of interest. In order to overcome this problem and exploit more of the spectral features of images, Discrete Wavelet Transform (DWT) is embedded into one of the most superior methods for object detection, which is Faster Region-based Convolutional Network (FRCNN). The accuracy of FRCNN is boosted by introducing the wavelet decomposition. The performance of the proposed strategy is tested, evaluated, and compared to the original FRCNN using two different datasets.