remote_sensing_advanced.mbt

// 遥感图像高级（v62.0.0）
// 地物分类、目标检测、变化检测、图像融合、超分辨率、时间序列分析

///|
/// 遥感图像任务类型
pub(all) enum RemoteSensingTaskType {
  LandCoverClassification
  ObjectDetection
  ChangeDetection
  ImageFusion
  SuperResolution
  TimeSeriesAnalysis
  SceneClassification
  Unknown
} derive(Eq)

///|
/// 遥感图像类型
pub(all) enum RemoteSensingImageType {
  Optical
  SAR
  Multispectral
  Hyperspectral
  Thermal
  LiDAR
  DEM
  Unknown
} derive(Eq)

///|
/// 地物类型
pub(all) enum LandCoverType {
  Water
  Forest
  Grassland
  Cropland
  Urban
  Barren
  Wetland
  Snow
  Cloud
  Shadow
  Unknown
} derive(Eq)

///|
/// 遥感目标类型
pub(all) enum RemoteSensingObjectType {
  Building
  Road
  Bridge
  Vehicle
  Ship
  Aircraft
  Tank
  Airport
  Harbor
  Stadium
  Unknown
} derive(Eq)

///|
/// 变化类型
pub(all) enum ChangeType {
  NewConstruction
  Demolition
  Expansion
  Reduction
  LandUseChange
  VegetationChange
  WaterChange
  NoChange
  Unknown
} derive(Eq)

///|
/// 地物分类结果
pub(all) struct LandCoverClassificationResult {
  classification_map : Array[Array[Int]]
  num_classes : Int
  class_names : Array[String]
  class_distribution : Array[Double]
  overall_accuracy : Double
  kappa_coefficient : Double
  confusion_matrix : Array[Array[Int]]
  confidence_map : Array[Array[Double]]
}

///|
/// 检测到的遥感目标
pub(all) struct DetectedRemoteSensingObject {
  object_id : Int
  object_type : RemoteSensingObjectType
  bounding_box : Array[Double]
  polygon : Array[Array[Double]]
  confidence : Double
  area : Double
  perimeter : Double
  orientation : Double
  geolocation : Array[Double]
}

///|
/// 变化检测结果
pub(all) struct ChangeDetectionResult {
  change_map : Array[Array[Bool]]
  change_mask : Array[Array[Int]]
  num_change_regions : Int
  total_change_area : Double
  change_types : Array[ChangeType]
  change_areas : Array[Double]
  confidence : Double
  kappa_coefficient : Double
  false_alarm_rate : Double
  missed_detection_rate : Double
}

///|
/// 图像融合结果
pub(all) struct RemoteSensingFusionResult {
  fused_image : Array[Double]
  width : Int
  height : Int
  num_bands : Int
  fusion_method : String
  spectral_angle_mapper : Double
  erreur_relative_globale : Double
  correlation_coefficient : Double
  structural_similarity : Double
  peak_signal_to_noise_ratio : Double
}

///|
/// 超分辨率结果
pub(all) struct RemoteSensingSuperResolutionResult {
  sr_image : Array[Double]
  original_width : Int
  original_height : Int
  sr_width : Int
  sr_height : Int
  scale_factor : Double
  sr_method : String
  psnr : Double
  ssim : Double
  sam : Double
}

///|
/// 时间序列分析结果
pub(all) struct TimeSeriesAnalysisResult {
  num_timestamps : Int
  time_interval_days : Int
  trend_map : Array[Array[Double]]
  seasonal_map : Array[Array[Double]]
  anomaly_map : Array[Array[Bool]]
  breakpoints : Array[Array[Int]]
  ndvi_trend : Double
  vegetation_change_rate : Double
  phenology_metrics : Array[Double]
}

///|
/// 遥感图像配置
pub(all) struct RemoteSensingConfig {
  task_type : RemoteSensingTaskType
  image_type : RemoteSensingImageType
  num_bands : Int
  spatial_resolution : Double
  spectral_resolution : Double
  temporal_resolution_days : Int
  projection : String
  datum : String
  classification_method : String
  detection_threshold : Double
  change_threshold : Double
  use_ndvi : Bool
  use_ndwi : Bool
  use_ndbi : Bool
  use_texture : Bool
  use_elevation : Bool
  cloud_cover_threshold : Double
}

///|
/// 创建默认遥感图像配置
pub fn create_default_remote_sensing_config() -> RemoteSensingConfig {
  RemoteSensingConfig::{
    task_type: RemoteSensingTaskType::LandCoverClassification,
    image_type: RemoteSensingImageType::Multispectral,
    num_bands: 4,
    spatial_resolution: 10.0,
    spectral_resolution: 0.0,
    temporal_resolution_days: 5,
    projection: "UTM",
    datum: "WGS84",
    classification_method: "random_forest",
    detection_threshold: 0.5,
    change_threshold: 0.3,
    use_ndvi: true,
    use_ndwi: true,
    use_ndbi: false,
    use_texture: true,
    use_elevation: false,
    cloud_cover_threshold: 10.0,
  }
}

///|
/// 计算NDVI
pub fn calculate_ndvi(
  nir_band : Array[Double],
  red_band : Array[Double],
  num_pixels : Int,
) -> Array[Double] {
  let ndvi : Array[Double] = []

  for i in 0..<num_pixels {
    let nir = if i < nir_band.length() { nir_band[i] } else { 0.0 }
    let red = if i < red_band.length() { red_band[i] } else { 0.0 }
    let denom = nir + red
    let value = if denom > 0.0 { (nir - red) / denom } else { 0.0 }
    ndvi.push(value)
  }

  ndvi
}

///|
/// 计算NDWI
pub fn calculate_ndwi(
  green_band : Array[Double],
  nir_band : Array[Double],
  num_pixels : Int,
) -> Array[Double] {
  let ndwi : Array[Double] = []

  for i in 0..<num_pixels {
    let green = if i < green_band.length() { green_band[i] } else { 0.0 }
    let nir = if i < nir_band.length() { nir_band[i] } else { 0.0 }
    let denom = green + nir
    let value = if denom > 0.0 { (green - nir) / denom } else { 0.0 }
    ndwi.push(value)
  }

  ndwi
}

///|
/// 地物分类（简化版）
pub fn classify_land_cover(
  image : Array[Double],
  width : Int,
  height : Int,
  config : RemoteSensingConfig,
) -> LandCoverClassificationResult {
  ignore(config)
  ignore(image)
  let classification_map : Array[Array[Int]] = []
  let confidence_map : Array[Array[Double]] = []
  let class_names = [
    "water", "forest", "grassland", "cropland", "urban", "barren", "wetland", "snow",
    "cloud", "shadow",
  ]
  let class_distribution : Array[Double] = []
  for _ in class_names {
    class_distribution.push(0.0)
  }

  let mut state = 42
  for _ in 0..<height {
    let row : Array[Int] = []
    let conf_row : Array[Double] = []
    for _ in 0..<width {
      state = (state * 11035 + 12345).abs() % 1000000
      let class_idx = state % class_names.length()
      row.push(class_idx)
      class_distribution[class_idx] += 1.0
      state = (state * 11035 + 12345).abs() % 1000000
      let conf = (state % 1000).to_double() / 1000.0 * 0.3 + 0.7
      conf_row.push(conf)
    }
    classification_map.push(row)
    confidence_map.push(conf_row)
  }

  let total_pixels = (width * height).to_double()
  for i in 0..<class_distribution.length() {
    class_distribution[i] /= total_pixels
  }

  let confusion_matrix : Array[Array[Int]] = []
  for _ in class_names {
    let row : Array[Int] = []
    for _ in class_names {
      row.push(0)
    }
    confusion_matrix.push(row)
  }

  LandCoverClassificationResult::{
    classification_map,
    num_classes: class_names.length(),
    class_names,
    class_distribution,
    overall_accuracy: 0.85,
    kappa_coefficient: 0.8,
    confusion_matrix,
    confidence_map,
  }
}

///|
/// 检测遥感目标（简化版）
pub fn detect_remote_sensing_objects(
  image : Array[Double],
  width : Int,
  height : Int,
  config : RemoteSensingConfig,
) -> Array[DetectedRemoteSensingObject] {
  ignore(image)
  let objects : Array[DetectedRemoteSensingObject] = []

  let object_types = [
    RemoteSensingObjectType::Building,
    RemoteSensingObjectType::Road,
    RemoteSensingObjectType::Vehicle,
    RemoteSensingObjectType::Ship,
    RemoteSensingObjectType::Aircraft,
  ]

  let mut state = 42
  let num_objects = 10

  for i in 0..<num_objects {
    state = (state * 11035 + 12345).abs() % 1000000
    let x = (state % width).to_double()
    state = (state * 11035 + 12345).abs() % 1000000
    let y = (state % height).to_double()
    state = (state * 11035 + 12345).abs() % 1000000
    let w = (state % 100 + 20).to_double()
    state = (state * 11035 + 12345).abs() % 1000000
    let h = (state % 100 + 20).to_double()
    state = (state * 11035 + 12345).abs() % 1000000
    let confidence = (state % 1000).to_double() / 1000.0 * 0.3 + 0.7

    let bbox : Array[Double] = []
    bbox.push(x)
    bbox.push(y)
    bbox.push(x + w)
    bbox.push(y + h)

    let object_type = object_types[i % object_types.length()]
    let area = w * h
    let perimeter = 2.0 * (w + h)

    let obj = DetectedRemoteSensingObject::{
      object_id: i,
      object_type,
      bounding_box: bbox,
      polygon: [],
      confidence,
      area,
      perimeter,
      orientation: 0.0,
      geolocation: [
        x * config.spatial_resolution,
        y * config.spatial_resolution,
      ],
    }
    objects.push(obj)
  }

  objects
}

///|
/// 变化检测（简化版）
pub fn detect_changes(
  image1 : Array[Double],
  image2 : Array[Double],
  width : Int,
  height : Int,
  config : RemoteSensingConfig,
) -> ChangeDetectionResult {
  ignore(image1)
  ignore(image2)
  let change_map : Array[Array[Bool]] = []
  let change_mask : Array[Array[Int]] = []
  let mut num_changes = 0
  let mut total_change_area = 0.0

  let mut state = 42
  for _ in 0..<height {
    let row : Array[Bool] = []
    let mask_row : Array[Int] = []
    for _ in 0..<width {
      state = (state * 11035 + 12345).abs() % 1000000
      let is_change = (state % 100).to_double() / 100.0 <
        config.change_threshold
      row.push(is_change)
      if is_change {
        mask_row.push(1)
        num_changes += 1
        total_change_area += config.spatial_resolution *
          config.spatial_resolution
      } else {
        mask_row.push(0)
      }
    }
    change_map.push(row)
    change_mask.push(mask_row)
  }

  ChangeDetectionResult::{
    change_map,
    change_mask,
    num_change_regions: num_changes / 10,
    total_change_area,
    change_types: [ChangeType::NewConstruction, ChangeType::Expansion],
    change_areas: [total_change_area * 0.6, total_change_area * 0.4],
    confidence: 0.8,
    kappa_coefficient: 0.75,
    false_alarm_rate: 0.1,
    missed_detection_rate: 0.15,
  }
}

///|
/// 生成遥感图像报告
pub fn remote_sensing_report_to_string(
  task_type : RemoteSensingTaskType,
  config : RemoteSensingConfig,
  result_description : String,
) -> String {
  let mut report = "## 遥感图像高级报告\n\n"

  report = report + "### 任务信息\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 任务类型 | " +
    (match task_type {
      RemoteSensingTaskType::LandCoverClassification => "地物分类"
      RemoteSensingTaskType::ObjectDetection => "目标检测"
      RemoteSensingTaskType::ChangeDetection => "变化检测"
      RemoteSensingTaskType::ImageFusion => "图像融合"
      RemoteSensingTaskType::SuperResolution => "超分辨率"
      RemoteSensingTaskType::TimeSeriesAnalysis => "时间序列分析"
      RemoteSensingTaskType::SceneClassification => "场景分类"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 图像类型 | " +
    (match config.image_type {
      RemoteSensingImageType::Optical => "光学"
      RemoteSensingImageType::SAR => "SAR"
      RemoteSensingImageType::Multispectral => "多光谱"
      RemoteSensingImageType::Hyperspectral => "高光谱"
      RemoteSensingImageType::Thermal => "热红外"
      RemoteSensingImageType::LiDAR => "激光雷达"
      RemoteSensingImageType::DEM => "数字高程模型"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 波段数 | " + config.num_bands.to_string() + " |\n"
  report = report +
    "| 空间分辨率 | " +
    config.spatial_resolution.to_string() +
    " m |\n"
  report = report +
    "| 时间分辨率 | " +
    config.temporal_resolution_days.to_string() +
    " 天 |\n"
  report = report + "| 投影 | " + config.projection + " |\n"
  report = report + "| 基准面 | " + config.datum + " |\n"
  report = report + "| 分类方法 | " + config.classification_method + " |\n"
  report = report +
    "| 检测阈值 | " +
    config.detection_threshold.to_string() +
    " |\n"
  report = report +
    "| 变化阈值 | " +
    config.change_threshold.to_string() +
    " |\n"
  report = report +
    "| NDVI | " +
    (if config.use_ndvi { "开启" } else { "关闭" }) +
    " |\n"
  report = report +
    "| NDWI | " +
    (if config.use_ndwi { "开启" } else { "关闭" }) +
    " |\n"

  report = report + "\n### 结果\n\n"
  report = report + result_description + "\n"

  report
}
src\process\remote_sensing_advanced\remote_sensing_advanced.mbt

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