Vegetation coverage detection from very high resolution satellite imagery

Automatic vegetation coverage detection plays a key role for monitoring and management of land usage, environmental variation, and urban planning. This paper presents a novel vegetation coverage detection technique for very high resolution multi-spectral satellite imagery. The proposed technique consists of two stages including a supervised patch-level scoring stage and an unsupervised pixel-level classification stage. In the first stage, a support vector regression (SVR) technique is developed which scores each image patch and generates a coarse patch-level vegetation map. In the second stage, an unsupervised pixel-level vegetation classification technique is developed, which produces a more detailed vegetation map by re-scoring those uncertain pixels based on the computed SVR scores. Experiments on very high resolution multi-spectral satellite images show that the proposed technique outperforms the state-of-the-art methods in both patch-level and pixel-level vegetation detection.