Satellite image time series are a worthwhile source of information for a broad range of applications, especially in the context of future global challenges. The challenge to discover correlations, patterns or anomalies would be eased if the data analysts might benefit of visualization tools enabling them to grasp, briefly, the characteristics of the time series. Responding to these needs, this paper proposes a graphical user interface focused on SITS visualization. While overcoming the current software limitations we developed a Python GUI interface enabling remote sensing scientists who use Python for their research, to visually analyze the satellite image time series in the same environment.

Remote sensing image classification is at the center of many tasks in the remote sensing domain. However, the complexity and content variety of aerial images contribute to making the task still challenging. Transformers have recently achieved state-of-the-art performances for numerous natural language processing and image processing tasks. In this paper, we pro-pose a novel solution towards remote sensing image classification based on a general-to-specific learning curve achieved through a cascaded chain of Vision Transformers (Vit). First, a standard pre-trained Vision Transformer (ViT) is used to provide general information regarding the remote sensing scenes, whereas the specific details are learned by means of a Convolutional Vision Transformer (CvT) which is trained end-to-end. The experiments conducted over two benchmark datasets of high resolution remote sensing images show the effectiveness of the proposed technique. Comparisons to other methods in the literature are also provided.

Remote sensing data is characterized by a large degree of heterogeneity and variability. In this context, change detection in heterogeneous bitemporal satellite images has become an emerging and important topic in order to ensure monitoring continuity and to reduce the probability of missing important events. In this paper, we propose an image translation method between the pre-event and post-event modalities that is based on a modified fractal coding technique that determines a dictionary of locations of self-similar structures. Self-similar structures are identified using a convolutional neural network (CNN) encoder as a mapping function between the compressed representations. The search for the k closest self-similar structures is performed using a vantage-point tree. Once the image modality translation is performed, the change map is obtained via an unsupervised Expectation-Maximization (EM) approach with spatial context constraints. The experiments conducted over several pairs of heterogeneous remote sensing images show the effectiveness of the proposed technique, if compared to other proposed methods.

The current technological developments lead to increased heterogeneity and variability in remote sensing imagery. In this context, unsupervised multimodal change detection techniques are mandatory to perform a continuous monitoring and rapid damage assessment by means of heterogeneous remote sensing data. Taking advantage of the latest advances in deep learning, we address multimodal change detection from an inter-modality image translation perspective. Inter-modality translation is achieved by means of generative adversarial networks built over U-Net architectures at both generator and discriminator levels and trained under CutMix transformations. A change prior is used to guide the learning process of the neural network framework and to reduce the impact of changed locations over the learned model. The change prior is derived in an unsupervised manner from comparisons between the post-event locations andknearest neighbor locations determined in the pre-event image. The experiments were conducted over several pairs of heterogeneous remote sensing images, and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed multimodal change detection framework.

Taking advantage of the latest advances in deep learning for seismology, we address earthquake characterization from a data-driven perspective. Many of the usual procedures for extracting information from seismograms require processing a large volume of data using empirical and physics rule-based techniques. In this letter, we propose a novel approach for estimating epicentral distance, depth, and magnitude directly from individual raw three-component seismograms of 1-min length observed by single stations. Our convolutional neural network-based method is able to handle complex-valued representations of the seismic data in the time–frequency domain by using dedicated convolutional and activation functions. In this way, our method benefits both from extracting relevant information through time–frequency domain analysis and from designing a single architecture that deals with complex information. The proposed method achieves a mean absolute error of 4.51 km for epicentral distance, 6.15 km for depth, and 0.26 for magnitude estimation. The experiments were conducted over a publicly available and large database, STanford EArthquake data set (STEAD), and the comparisons with current state-of-the-art approaches show the effectiveness of the proposed approach. Source code and best model are available athttps://github.com/ristea/stead-earthquake-cnn.

Due to the numerous technological developments, the last years have witnessed an increase in the diversity of remote sensing data, whereas the need to interpret multimodal remote sensing data emerged. In this article, we propose a new multimodal unsupervised change detection strategy that projects a pre-event image in the post-event imaging modality. From a reconstruction perspective, the blocks in the pre-event scene are rebuilt from denoised versions of post-event blocks by means of convolutional denoising autoencoders. In order to perform this reconstruction, a dictionary of locations of similar blocks is learned from the pre-event image by analyzing compressed representations. The experiments, conducted over remote sensing images acquired by different sensors, show the effectiveness and the reliability of the proposed approach in various scenarios reflecting diverse types of changes.

This paper presents a novel framework for multilabel classification of remote sensing images using Error-Correcting Output Codes (ECOC). Starting with a set of primary class labels, the proposed framework consists in transforming the multiclass problem into binary learning subproblems. The distributed output representations of these binary learners are then transformed into primary class labels. In order to obtain robustness with respect to scale, rotation and image content, a Bag-of-Visual Words (BOVW) model based on Scale Invariant Feature Transform (SIFT) descriptors is used for feature extraction. BOVW assumes an a-priori unsupervised learning of a dictionary of visual words over the training set. Experiments are performed on GeoEye-1 images and the results show the effectiveness of the proposed approach towards multilabel classification, if compared to other methods.

Satellite image time series are a valuable resource for enhancing land exploitation by respecting the natural cycles, analyzing urban expansion and its positive and negative effects, limiting the unhealthy rhythm of deforestation, understanding natural hazards and so on. In this context, understanding only the changes in multitemporal images is not sufficient. This paper aims to correlate multi-level change detection techniques with image semantic segmentation methods in order to build an hierarchy of changes for each semantic class. In this way, we are able to provide statistics regarding the levels of change suffered by a certain area. The methods are demonstrated with examples involving bi-temporal Land-sat images.

Several algorithms for polarimetric synthetic aperture radar (PolSAR) data indexing and classification were proposed in the state of the art literature. In particular, one of them computes powerful, compact feature descriptors composed of the first three logarithmic cumulants of the BiQuaternion Fractional Fourier Transform (BiQFrFT) coefficients of PolSAR patches. Since the BiQFrFT of each patch is computed at three different angles, the algorithm's result consists in nine complex-valued features (18 real-valued features) for single polarization images and in nine biquaternion-valued features (72 real-valued features) for fully polarimetric images. In this paper feature selection based on mutual information (MI) is employed to optimally select a subset of features, in order to improve the indexing performances and to minimize the classification error. The improved results are shown on two polarimetric images: a L-band PALSAR image over Danube's Delta, Romania and a C-band RadarSAT2 image over Brâila, Romania.

In this letter, we present a novel technique for unsupervised change analysis that leads to a method of ranking the changes that occur between two satellite images acquired at different moments of time. The proposed change analysis is based on binary descriptors and uses the Hamming distance as a similarity metric. In order to render a completely unsupervised solution, the obtained distances are further classified using vector quantization methods (i.e., Lloyd's algorithm for optimal quantization). The ultimate goal in the change analysis chain is to build change intensity maps that provide an overview of the severeness of changes in the area under analysis. In addition, the proposed analysis technique can be easily adapted for change detection by selecting only two levels for quantization. This discriminative method (i.e., between changed/unchanged zones) is compared with other previously developed techniques that use principal component analysis or Bayes theory as starting points for their analysis. The experiments are carried on Landsat images at a 30-m spatial resolution, covering an area of approximately 59×51 km2over the surroundings of Bucharest, Romania, and containing multispectral information.