# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 1 Introduction  
Spatial and temporal resolutions of satellite images have been greatly improved due to continuous development of space technologies and several innovative satellite constellation programs [1, 2, 3] aiming for near real-time earth observation for defense, security, etc. Accordingly, interest in the application of satellite images has increased with the expectation of abundant high-resolution image acquisitions. The improved spatial resolution brings a diversity of observable objects. Improved temporal resolution offers various new applications based on detection of changes in time-series satellite images. While change detection from satellite images has focused on changes in relatively large areas, such as land use and land cover [4, 5, 6, 7], forest [8, 9], disaster damaged area [10, 11], wetland [12], and coastal areas [13]. The improved spatial and temporal resolutions should enable change detection of small objects such as vehicles, roads [14], and buildings [15, 16, 17]. Traditional change detection research has limited observable objects due to the resolution limit of satellite sensors. We wish to conduct a change detection study on small objects including vehicles using high-resolution satellite images. Due to the agile nature of small objects of interest, change in this paper mainly means appearance and disappearance between previous and recent images. Change detection can be classified by the unit of change determination. Traditionally, changes are estimated on individual image pixels (pixel-based approaches) or a group of pixels (object-based approaches) [18; 19; 20; 21]. Pixel-based approaches estimate change magnitude per pixel between time-series images by comparing brightness numbers of a pixel and its neighbors, and determine changed pixels by thresholding the change magnitude. Object-based approaches [22] define objects by grouping image pixels with similar properties or by using pre-determined templates [4; 5]. Changes are estimated by considering pixel-based change magnitude and intrinsic group properties [23; 24]. Recently, [PERSON] et al. [25] introduced object-based change detection using various features as change detection units: textural, deep, object-based and angular. Textural features included grey level correlation [26; 27], morphological profiles [28; 29; 30], and Gabor and wavelet features [31]. Deep features refer to change analysis units in convolutional neural networks [14; 32]. Angular features use surface geometry factors such as elevation and observation angles [33; 34]. While pixel-based approaches can determine change intuitively and quickly [19; 20], they may generate noisy results and be vulnerable to radiometric and geometric consistency in time-series images. Object-based approaches can overcome the problems of pixel-based approaches [22; 23]. However, they require additional processing for object extraction and analysis [24]. In addition, traditional object-based approaches have been applied mostly to large-sized objects. In this paper, we aim to develop a method for change detection of small objects that can overcome the problems of pixel-based and object-based approaches. To achieve this, we propose the use of feature points, often referred to as interest points or keypoints [35; 36], as change detection units. They can be extracted automatically and without heavy image computation [35; 36], and matched with each other through various feature matching techniques [37; 38; 39]. We can perform feature-based change detection by extracting feature points from previous and recent images and by estimating their change magnitude. We estimate the change magnitude of feature points by calculating pixel-based change magnitude first and counting the ratio of changed pixels around the extracted features. We also apply feature matching, and classify matched feature points as unchanged. This should reduce false alarms. The remaining feature points are judged as changed or unchanged based on their change magnitude.