Machine learning for satellite and aerial imagery: hyperspectral and multispectral fusion, change detection, and multi-sensor pipelines. Coverage focuses on what makes these methods reliable when the sensors disagree and the scenes change.
Train an object detector on ships, planes, and storage tanks, then fly it over a harbor it has never seen. A crane swings into view. The detector has two bad options and one good one. It can force the crane…
HyRS-OWOD: Hyperbolic Geometry for Open World Detection in Remote Sensing Read More »
Hyperspectral anomaly detection has a strange property compared with most computer vision tasks. You are not told what to look for. There is no labeled set of tanks, tumors or storm damage to learn from. Instead the algorithm has to…
Why Equal Weighted Hyperspectral Anomaly Fusion Fails Read More »
Picture a satellite passing over Dhaka just after ten in the morning local time, quietly recording how hot the ground has become beneath one of the densest cities on the planet. It does this again and again for ten straight…
Mapping a Decade of Dhaka Urban Heat Risk From Orbit Read More »
Extracting road networks automatically from satellite and airborne imagery has been a live research problem for decades, and the authors note that a search of the Web of Science database turns up roughly ten thousand papers on the topic. The…
Improving Road Network Extraction via Multisource Fusion and Evidential Reasoning Read More »
Wind speed forecasting has a long history built on physical simulation, statistical time series methods, and more recently deep networks. Global wind capacity keeps climbing, and China alone added 80 gigawatts in 2024, which the paper notes was roughly seventy…
Adaptive Graph Attention Improves Wind Vector Forecasting Read More »
Picture a rooftop and a road sitting a few pixels apart in a satellite scene, both gray, both flat looking in a spectral plot, both the kind of thing that trips up a classifier trained on color alone. A sensor…
Training a radar recognition model would be easy if you had thousands of labeled real images of every target you care about. In practice you rarely do, because the interesting targets are exactly the ones nobody hands you clean data…
Universal Domain Adaptation for SAR Target Recognition Read More »
Infrared small target detection sits behind a wide range of practical uses, some civilian, some defense related, spanning maritime search and rescue, early wildfire warning, vehicle and drone monitoring, reconnaissance, and precision guidance, exactly as the researchers themselves describe the…
DeepPro: Revolutionizing Infrared Small Target Detection Read More »
MVR-GLF extracts three complementary spectral views from temporal hyperspectral images and fuses them globally and locally, catching changes that single-view detectors miss.
How MVR-GLF Reads Spectral Shape to Catch What Others Miss Read More »
SMM-FNet fuses hyperspectral, multispectral, and radar imagery through purpose-built Mamba modules, showing why three sensor sources beat two in remote sensing.
When Two Sensors Are Not Enough: SMM-FNet Fuses Three Remote Sensing Sources With Mamba Read More »