Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

The global deforestation rate continues to worsen each year, and will eventually lead to various negative consequences for humans and the environment. It is essential to develop an effective forest monitoring system to detect any changes in forest areas, in particular, by monitoring the progress of forest conservation efforts. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. The previously implemented forest monitoring systems were ineffective due to their use of low-resolution satellite images and the inefficiency of drone-based data that offer a limited field of view. Most government agencies also still rely on manual annotation, which makes the monitoring process time-consuming, tedious, and expensive. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model, which allows the model to effectively segment forest areas regardless of their varying sizes, patterns, and colors. To investigate the impact of SPP on the forest segmentation system, a set of experiments was conducted by integrating several variants of SPP ranging from two to four parallel paths with different combinations of pooling kernel size, placed at the bottleneck layer of the U-Net model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy (acc), intersection over union (IoU), and F1-score (F1score), respectively. The best SPP variant consists of four parallel paths with a combination of pooling kernel sizes of 2x2, 4x4, 6x6, and 8x8 pixels that produced the highest acc, IoU, and F1score of 86.71%, 75.59%, and 82.88%, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas.