Automatic detection of surface deformation features from a large volume of Interfero16 metric Synthetic Aperture Radar (InSAR) data is challenging because the magnitude of InSAR measurement noise varies substantially in both space and time. In this work, we present a computer vision algorithm based on Laplacian of Gaussian (LoG) filtering for detecting the size and location of unknown surface deformation features. Because our algorithm detects spatially coherent features, tropospheric noise artifacts that share similar spatial characteristics may also be detected. We estimate the tropospheric noise spectrum directly from data, which allows us to simulate new instances of noise that resemble the actual InSAR observations. Based on these simulations, we quantify the likelihood that a detected feature is a real deformation signal. We demonstrate the performance of our algorithm using Sentinel-1 data acquired between 2014 and 2019 over the ∼ 80,000 km2 oil-producing Permian Basin in West Texas, one of the most productive oil fields in the world. We detect clusters of deformation features associated with oil production, wastewater injection, and fault activities. The number of detected deformation features increases substantially over the study period, which is consistent with the over-all rise in oil production within the Permian Basin since 2014. Our algorithm is robust and flexible, and can be integrated to various multi-temporal InSAR time series methods for detecting a broad range of deformation features.