We propose an unsupervised deep convolutional coupling network for change detection based on two heterogeneous images acquired by optical sensors and radars different dates. Most existing methods are homogeneous images. Due to the complementary properties of radar sensors, there is increasing interest in The proposed symmetric with each side consisting one layer several layers. input connected sides network, respectively, transformed into a feature space where their representations become...

Abstract In recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across globe. Emergency response rapid for mitigation calls effective approaches near real-time wildfire monitoring. Capable of penetrating clouds smoke, imaging day night, Synthetic Aperture Radar (SAR) can play a critical role this communication, we investigated demonstrated potential Sentinel-1 SAR time series with deep learning framework progression The...

With the rapid technological development of various satellite sensors, high-resolution remotely sensed imagery has been an important source data for change detection in land cover transition. However, it is still a challenging problem to effectively exploit available spectral information highlight changes. In this paper, we present novel framework remote sensing images, which incorporates superpixel-based feature extraction and hierarchical difference representation learning by neural...

Due to the noise interference and redundancy in multispectral images, it is promising transform available spectral channels into a suitable feature space for relieving reducing redundancy. The booming of deep learning provides flexible tool learn abstract invariant features directly from data their raw forms. In this letter, we propose an unsupervised change detection technique which combine belief networks (DBNs) analysis highlight changes. First, DBN established capture key information...

Change detection can be treated as a generative learning procedure, in which the connection between bitemporal images and desired change map modeled one. In this letter, we propose an unsupervised method based on adversarial networks (GANs), has ability of recovering training data distribution from noise input. Here, joint two to detected is taken input initial difference image (DI), generated by traditional such vector analysis, used provide prior knowledge for sampling Bayesian theorem...

With the increase of multisource data available from remote sensing platforms, it is demanding to develop unsupervised techniques for change detection data. The difference in imaging mechanism makes difficult carry out a direct comparison between original observation spaces. Different sensors provide different descriptions on same truth low-dimension spaces, but indicates comparability some high-dimensional feature Inspired by this, we try solve this problem transforming into common...

Wildfires are increasing in intensity and frequency across the globe due to climate change rising global temperature. Development of novel approach Monitor wildfire progressions near real-time is therefore critical importance for emergency responses. The objective this research investigate continuous learning with U-Net by exploiting both Sentinel-1 SAR Sentinel-2 MSI time series accuracy progression mapping. In study, optical-based burned areas prior each acquisition (when available) were...

Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial comprehensive information for stabilization recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g., dNBR, RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort prior knowledge visual inspection to determine burn thresholds. In this study, we aim...

Wildfires play a crucial role in the transformation of forest ecosystems and exert significant influence on global climate over geological timescales. Recent shifts patterns intensified human–forest interactions have led to an increase incidence wildfires. These fires are characterized by their extensive coverage, higher frequency, prolonged duration, rendering them increasingly destructive. To mitigate impact wildfires change, ecosystems, biodiversity, it is imperative conduct systematic...

Despite the popularity and success in burned area detection assessment, multispectral satellite images are often affected by poor sunlight-illumination conditions, particularly at high latitudes. Given that Synthetic Aperture Radar (SAR) can effectively penetrate clouds collect all-weather conditions during day night, complementary use of optical SAR data be helpful for remote-sensing measurements assessments sites. Nevertheless, widely used burn-sensitive spectral indices hardly help...

With the rapid increase of remote sensing images in temporal, spectral, and spatial resolutions, it is urgent to develop effective techniques for joint interpretation spatial-temporal images. Multitype change detection (CD) a significant research topic multitemporal image analysis, its core effectively measure difference degree represent among In this paper, we propose novel representation learning (DRL) network present an unsupervised framework multitype CD task. Deep neural networks work...

Cross-view geo-localization is a critical task in various applications, such as smart city management and disaster monitoring. Current methods typically divide satellite image into patches use these to identify the geographic location of query image. However, can only provide an rather than specific object interest. This makes it difficult link GeoDatabases obtain detailed information about target object, its name construction time. To overcome this limitation, we propose novel problem...

Previous studies have shown that Synthetic Aperture Radar (SAR) is able to detect burned areas, serving as a key data source for monitoring active wildfires in situations where optical sensors are hindered by dense smoke or cloud cover. remote sensing provides rich and useful on historical which critical large-scale wildfire area mapping. This study aims unveil the potential inherent Sentinel-1 C-Band SAR investigate impact of reference masks images frequently exhibit disruptive speckle...

Two new monoterpenoid indole alkaloids, named 14,15-dihydro-14β,15β-epoxy-10-hydroxyscandine (1) and 15α-hydroxy-meloscandonine (2), together with 12 known compounds, were isolated from the aerial parts of Melodinus hemsleyanus Diels. The structures 1 2 elucidated on bases 1D 2D NMR spectra MS. compounds evaluated for their PTP1B Drak2 inhibitory effects, inactivity.