Tilt Correction Toward Building Detection of Remote Sensing Images

Abstract

Building detection is a crucial task in the field of remote sensing, and which can facilitate urban construction planning, disaster survey, and emergency landing. However, for large-size remote sensing images, the great majority of existing works have ignored the image tilt problem. This problem can result in partitioning buildings into separately oblique parts when the large-size images are partitioned. This is not beneficial to preserve semantic completeness of the building objects. Motivated by the fact above, we firstly propose a framework that detecting objects in large-size image, particularly for the building detection. The framework mainly consists of two phases. In the first phase, we particularly propose a Tilt Correction (TC) algorithm which contains three steps: texture mapping, tilt angle assessment, and image rotation. In the second phase, the building detection is performed with object detectors, especially deep neural network based methods. Last but not least, the detection results will be inversely mapped to the original large-size image. Furthermore, a challenging dataset named Aerial Image Building Detection (AIBD) is contributed for the public research. To evaluate the TC method, we also define an evaluation metric to compute the Cost of Building Partition (CoBP). The experiment results demonstrate the effects of the proposed method for the building detection.

A perfect probe: Resonance of underdamped scaled Brownian motion

“…Consequently, there recently have been various works about resonance in various systems ranging from the Fermi scale to the galactic size, such as nuclear magnetic resonance [7], the resonance of electron [8], molecules [9], atoms [10], etc. The importance of such devices is that they would provide a blueprint for detecting anomalous phenomena in various fields, such as phase response curves of earthquakes [11], building detection of remote sensing images [12], etc. In statistical physics, there have been many theoretical and experimental studies of the (a) E-mail: zchh2009@126.com (corresponding author) interaction between a Brownian particle and its environment or other particles in various systems.…”

Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection

“…Three publicly available object-detection datasets of remote sensing images, namely DIOR [ 34 ], HRRSD [ 35 ], and AIBD [ 36 ], are used to evaluate the proposed methods in the experiments. Some examples of DIOR, HRRSD, and AIBD are shown as Figure 7 .…”

Snapshot depth–spectral imaging based on image mapping and light field

“…Multidimensional imaging technique can acquire plenty of optical information of scenes as much as nine dimensions (x, y, z, θ, φ, ψ, χ, λ, t), including three-dimensional (3D) spatial intensity distribution (x, y, z), propagation polar angles (θ, φ), propagations (ψ, χ), wavelength (λ) for spectral intensity and time (t) [1]. The multidimensional imaging has a variety of applications in astrophysics, remote sensing, security, and biochemistry et al [2][3][4][5][6]. Especially, 3D spatial distribution and one-dimensional (1D) spectral intensity is significant in target detection, recognition, tracking, scene classification and other computer vision fields et al [7][8][9][10].…”

“…Fig 6. The simulation raw data collected by the detector of the SDSIIL, a is the entire raw data, b is one of the sub images, c is the enlarged view of the − 2 mm depth region, d is the enlarged view of the 0 mm depth region, e is the enlarged view of the 2 mm depth region, f is the enlarged view of sub pupil image behind some microlens…”