Optimal longwave infrared (LWIR) scene contrast occurs when reflections from other sources are minimized, leaving only thermal emission. Applying contrast enhancement to LWIR imagery based on pixel values' spatial distribution without regard to underlying temperature and emissivity is a non-physics-based approach. For a physics-based approach, something must be known about the temperature or the objects' emissivity under observation. In remote sensing applications, atmospheric conditions are measured allowing for calculated values for downwelling and path radiance to be obtained. Then, an iterative process can be performed using well-established TES algorithms to determine temperature and emissivity within specific bands. In this paper, we propose a method using a three-band LWIR imaging system with a partial sky view to collect in-scene data to apply contrast enhancement based on spectral differences between bands. Unlike traditional contrast enhancement methods, temperature variations between each band are considered and implemented using relatively inexpensive uncooled microbolometer cameras. We detail the process used for calibrating and determining brightness temperatures with sub-band LWIR filtered cameras. Using absolute sky radiance correlated to MODTRAN6 models, we estimate objects' emissivity profiles in a scene and propose an algorithm for applying contrast enhancement.